{
"cells": [
{
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"source": [
"# Kalman filter\n",
"\n",
""
]
},
{
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"metadata": {
"slideshow": {
"slide_type": "slide"
}
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"source": [
"## Overview\n",
"\n",
"This lecture will cover the following topics:\n",
"- Introduction to the Kalman Filter.\n",
"- Model components and assumptions.\n",
"- The Kalman Filter algorithm.\n",
"- Application to static and dynamic one-dimensional data.\n",
"- Application to higher-dimensional data."
]
},
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"slide_type": "skip"
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"hide-input"
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"source": [
"# Imports\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import cv2\n",
"np.random.seed(0) # for reproducibility"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
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"source": [
"## What is a Kalman Filter?"
]
},
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"cell_type": "markdown",
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"- The Kalman Filter (KF) is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies.\n",
"- It produces estimates of unknown variables that tend to be more accurate than those based only on measurements.\n",
"- Developed by Rudolf E. Kálmán in the late 1950s.\n",
"- Applications:\n",
" - tracking objects (Apollo project, GPS, self driving cars).\n",
" - image processing.\n",
" - economic and financial modeling."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### Theoretical foundations\n",
"\n",
"- Dynamic systems are systems that change over time.\n",
"- They are described by a set of equations that predict the future state of the system based on its current state.\n",
"- The KF assumes the system is a *linear* dynamic system with *Gaussian* noise. \n",
"- Gaussian distributions are used because of their nice properties when dealing with averages and variances."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### What are the ingredients?\n",
"\n",
"**State** \n",
"- The true value of the variables we want to estimate. \n",
"- The state vector represents all the information needed to describe the current state of the system.\n",
"\n",
"**Observation model** \n",
"- Relates the current state to the measurements or observations."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"**Noisy measurements** \n",
"- The process noise and measurement noise (assumed to be Gaussian) represent the uncertainty in our models and measurements. "
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"Example:\n",
"- Car moving at a constant velocity.\n",
"- Model: Car position = velocity $\\times$ time $+$ system noise.\n",
"- Measurements: position and velocity (which are also noisy)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### The role of noise\n",
"\n",
"The tradeoff between the influence of the model and the measurements is determined by noise.\n",
"\n",
"- If the model has relatively large errors, more importance is given to the latest measurements in computing the current estimate.\n",
"- If the measurement have larger errors, more importance is given to the model in making the current estimate.\n",
"- Therefore, you need to estimate not only your state but also the errors (the covariance) for both the model and the measurements.\n",
"- These must be updated at each time step, too."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Update strategy\n",
"\n",
"KF is a recursive algorithm:\n",
"\n",
"- Uses information from previous time step to update the estimates.\n",
"- Does not keep in memory all the data acquired so far.\n",
"- Has predictor-corrector structure:\n",
" - Make a prediction based on the model.\n",
" - Update the prediction with the measurements.\n",
" - Repeat."
]
},
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"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### Fundamental assumptions\n",
"\n",
"The Kalman filter approach is based on three fundamental assumptions:\n",
"\n",
"1. The system can be described or approximated by a linear model.\n",
"2. All noise (from both the system and the measurements) is white, i.e., the values are not correlated.\n",
"3. All noise is Gaussian."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"#### Assumption 1: linearity\n",
"\n",
"Each variable at the current time is a linear function of the variables at previous times.\n",
"\n",
"- Many systems can be approximated this way.\n",
"- Linear systems are easy to analyze.\n",
"- Nonlinear systems can often be approximated by linear models around a current estimate (extended KF)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"#### Assumption 2: Whiteness\n",
"\n",
"The noise values are not correlated in time.\n",
"\n",
"- If you know the noise at time $t$, it doesn't help you to predict the noise at future times $t+\\tau$.\n",
"- White noise is a reasonable approximation of the real noise.\n",
"- The assumption makes the mathematics tractable."
]
},
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"cell_type": "markdown",
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"slide_type": "subslide"
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"```{note}\n",
"White noise contains a mix of all the different frequencies at the same intensity blended together. \n",
"\n",
"This is similar to how white light contains all the colors of the rainbow combined.\n",
"```"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"#### Assumption 3: Gaussian noise\n",
"\n",
"At any point in time, the probability density of the noise is a Gaussian.\n",
"\n",
"- System and measurement noise are often a combination of many small sources of noise. \n",
"- The combination effect is approximately Gaussian.\n",
"- If only mean and variance are known (typical case in engineering systems), Gaussian distribution is a good choice as these two quantities completely determine the Gaussian distribution.\n",
"- Gaussian distribution have nice properties and are easy to treat mathematically.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Technical details"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### Combining two sources\n",
"\n",
"Assume a car has a certain initial position $x_0=0$ and initial velocity $\\dot{x}_0=60 km/h$.\n",
"\n",
"If the speed is constant, we have:\n",
"\n",
"$$x_{t} = 1\\cdot x_{t-1} + \\delta t \\cdot \\dot{x}_{t-1}$$\n",
"$$\\dot{x}_{t} = 0 \\cdot x_{t-1} + 1 \\cdot \\dot{x}_{t-1}$$\n"
]
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"slide_type": "subslide"
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"In matrix form:\n",
"\n",
"$$ \\boldsymbol{x}_{t} = \n",
"\\begin{bmatrix}\n",
"x_{t} \\\\\n",
"\\dot{x}_{t}\n",
"\\end{bmatrix}\n",
"=\n",
"\\begin{bmatrix}\n",
"1 & \\delta t \\\\\n",
"0 & 1 \n",
"\\end{bmatrix}\n",
"\\begin{bmatrix}\n",
"x_{t-1} \\\\\n",
"\\dot{x}_{t-1}\n",
"\\end{bmatrix} = \\mathbf{A} \\boldsymbol{x}_{t-1}\n",
"$$\n",
"\n",
""
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"- The current position and speed will evolve according to this simple dynamical system \n",
"\n",
"$$\\boldsymbol{x}_{t} = \\mathbf{A} \\boldsymbol{x}_{t-1} + \\boldsymbol{w}_{t-1}$$ \n",
"\n",
"- where $\\boldsymbol{w}_t \\sim \\mathcal{N}(0,\\mathbf{Q})$ represents perturbations in the underlying dynamical systems (e.g., holes on the road).\n",
"- $\\boldsymbol{x}_{t} \\in \\mathbb{R}^{N}$ represents the state of the system.\n",
"- $\\mathbf{Q} \\in \\mathbb{R}^{N \\times N}$ is the *process noise covariance*, where $N$ represents the number of state variables (2 in our case, position and speed)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"Where the car will be after 1 minute?\n",
"\n",
"If we rely only on our simple sistem $\\boldsymbol{x}_{t+1} = \\mathbf{A} \\boldsymbol{x}_{t}$ without even consider the noise, the car will keep moving at $60 km/h$ and will be at $1 km$ distance.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Let say our car has a GPS.\n",
"- As all instruments, our GPS will be affected by errors and gives us measurements with uncertainties in it.\n",
"- In addition, our GPS can only measure the position but not the speed.\n",
"- Based on our GPS, after 1 minute we are at $0.8 km$ from where we started.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"- Let the measure of our GPS be \n",
"\n",
"$$\\boldsymbol{z}_t = \\mathbf{H} \\boldsymbol{x}_t + \\boldsymbol{v}_t$$ \n",
"\n",
"- where $\\boldsymbol{v}_t \\sim \\mathcal{N}(0,\\mathbf{R})$ represents the uncertainty in the measures.\n",
"- $\\boldsymbol{z}_{t} \\in \\mathbb{R}^{M}$ is the measurements vector.\n",
"- In our case, since the GPS measures only the position, we have $\\mathbf{H} = \\begin{bmatrix} 1 & 0 \\end{bmatrix}$ and $\\mathbf{R} = \\begin{bmatrix} r_{xx} \\end{bmatrix}$.\n",
"- In general, $\\mathbf{H} \\in \\mathbb{R}^{M,N}$ is a matrix that maps the $N$ state variables into the $M$ measurements and $\\mathbf{R} \\in \\mathbb{R}^{M,M}$ is the measurement noise covariance."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"In summary, we have two different sources of information:\n",
"- a linear stochastic difference equation, representing our imprecise knowledge of a discrete-time controlled process;\n",
"- a source of measurements, that are noisy.\n",
"\n",
"\n",
"\n",
"How do we combine them to get the best possible estimate of our system variables?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"### The KF algorithm\n",
"\n",
"The KF is an interative procedure that consists of two steps: \n",
"\n",
"- **Predict** (Time update)\n",
"- **Correct** (Measurement update)\n",
"\n",
"These two steps are used to update two quantities:\n",
"- The **state estimate**.\n",
"- The uncertainty about our state estimate, called **covariance estimate**.\n",
"\n",
"Before looking at the iterative procedure let's introduce this second quantity."
]
},
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"slideshow": {
"slide_type": "subslide"
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"**Covariance estimate**\n",
"\n",
"There are two sources of error in the estimate of the state of our system.\n",
"\n",
"- The *prior* estimate error $\\boldsymbol{e}^{-}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}^{-}_t$.\n",
"- The *posterior* estimate error $\\boldsymbol{e}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}_t$.\n",
"\n",
"Where:\n",
"- $\\hat{\\boldsymbol{x}}^{-}_t$ is the estimate of the state based only on the knowledge of the system, e.g., the dynamics equations.\n",
"- $\\hat{\\boldsymbol{x}}_t$ is the estimate based also on the measurement $\\boldsymbol{z}_t$, e.g., the GPS. "
]
},
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "subslide"
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"Each type of error is associated with a covariance matrix, which reflects the amount of uncertainty in the state estimates.\n",
"\n",
"- $\\mathbf{P}_t^{-} = \\mathbb{E}[\\boldsymbol{e}^{-}_t \\boldsymbol{e}^{-T}_t] \\in \\mathbb{R}^{N\\times N}$ (*prior* covariance estimate).\n",
"- $\\mathbf{P}_t = \\mathbb{E}[\\boldsymbol{e}_t \\boldsymbol{e}^{T}_t] \\in \\mathbb{R}^{N\\times N}$ (*posterior* covariance estimate).\n",
"- In our case, we have $\\mathbf{P}_t = \\begin{bmatrix} p_{xx} & p_{\\dot x x} \\\\ p_{\\dot x x} & p_{\\dot x \\dot x} \\end{bmatrix}$ (for readability, the index $t$ on the matrix elements is omitted):\n",
" - $p_{xx}$ is the uncertainty about the position;\n",
" - $p_{\\dot x \\dot x}$ is the uncertainty about the velocity;\n",
" - $p_{x \\dot x}$ and $p_{\\dot x x}$ represent the correlation between the noise in the measurements of the position and the speed.\n",
"- $\\mathbf{P}_t^{-}$ has the same form."
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Predict\n",
"\n",
"- The dynamics equations are responsible for projecting forward (in time) the current state and error covariance estimates to obtain the *prior* estimate for the next time step.\n",
"- Such time update equations can also be thought of as *predictor equations*.\n",
"\n",
"State estimate update: \n",
"\n",
"$$\\hat{\\boldsymbol{x}}_{t}^{-} = \\mathbf{A} \\hat{\\boldsymbol{x}}_{t-1}$$\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"Covariance estimate update: \n",
"\n",
"$$\n",
"\\begin{aligned} \n",
"\\mathbf{P}_{t}^{-} &= \\mathbb{E}[\\boldsymbol{e}^{-}_t \\boldsymbol{e}^{-T}_t] \\\\\n",
" &= \\mathbb{E}[(\\boldsymbol{x}_{t} - \\hat{\\boldsymbol{x}}^{-}_{t})(\\boldsymbol{x}_{t} - \\hat{\\boldsymbol{x}}^{-}_{t})^T] \\\\\n",
" &= \\mathbb{E}[(\\mathbf{A} \\boldsymbol{x}_{t-1} + \\boldsymbol{w} - \\mathbf{A} \\hat{\\boldsymbol{x}}_{t-1})(\\mathbf{A} \\boldsymbol{x}_{t-1} + \\boldsymbol{w} - \\mathbf{A} \\hat{\\boldsymbol{x}}_{t-1})^T] \\\\\n",
" &= \\mathbb{E}[(\\mathbf{A}\\boldsymbol{e}_{t-1})(\\boldsymbol{e}_{t-1}^T \\mathbf{A}^T)] + \\mathbb{E}[\\boldsymbol{w}\\boldsymbol{w}^T]\\\\\n",
" &= \\mathbf{A}\\mathbb{E}[\\boldsymbol{e}_{t-1}\\boldsymbol{e}_{t-1}^T ]\\mathbf{A}^T + \\mathbf{Q} \\\\\n",
" &= \\mathbf{A}\\mathbf{P}_{t-1}\\mathbf{A}^T + \\mathbf{Q}\n",
"\\end{aligned}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{important}\n",
"- Both the state $\\boldsymbol{x}$ and the measurements $\\boldsymbol{z}$ are modeled as random variables (with associated process and measurement noise). \n",
"- As a result, the errors $\\boldsymbol{e^{-}}$ and $\\boldsymbol{e}$ are random variables themselves.\n",
"- The expectation $\\mathbb{E}[\\cdot]$ is taken over the probability distribution governing these random variables.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"In summary, the Predict step consists of two equations to update:\n",
"- State estimate.\n",
"- Covariance estimate.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Correct\n",
"\n",
"The measurement update equations are responsible for the feedback, i.e. for incorporating a new measurement into the prior estimate to obtain an improved posterior estimate.\n",
"\n",
"The posterior estimate $\\hat{\\boldsymbol{x}}_{t}$ is a linear combination of:\n",
"- The prior estimate $\\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"- A weighted difference between the actual measurement $\\boldsymbol{z}_{t}$ and a measurement prediction $\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"\n",
"$$\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{K}_t(\\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t})$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- The difference $\\Delta_t = \\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$ is called measurement *innovation* or *residual*.\n",
"- $\\Delta_t$ reflects the difference between our imprecise prediction ($\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$) and the actual noisy measurement ($\\boldsymbol{z}_{t}$).\n",
"- A residual of zero means that the two are in complete agreement.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"The matrix $\\mathbf{K} \\in \\mathbb{R}^{N \\times M}$ is chosen to be the **gain** that minimizes the posterior error covariance.\n",
"\n",
"Derivation (sketch):\n",
"1. Define the posterior error $\\boldsymbol{e}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}_t$.\n",
"2. Define the posterior error covariance $P_t = \\mathbb{E}[\\boldsymbol{e}_t \\boldsymbol{e}^{T}_t]$.\n",
"3. Substitute $\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{K}\\Delta_t$ in 1).\n",
"4. Substitute the $e_t$ obtained from 3) in 2) and take the expectation.\n",
"5. Take the derivative of the trace of the result with respect to $\\mathbf{K}$.\n",
"6. Set the result equal to zero.\n",
"7. Solve for $\\mathbf{K}$."
]
},
{
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"metadata": {
"slideshow": {
"slide_type": "subslide"
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"The previous derivation give us the following result:\n",
"\n",
"$$\\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T (\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1}$$\n",
"\n",
"Back to our car example, we have:\n",
"\n",
"$$\\mathbf{K}_t = \\frac{\\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\dot x x} \\\\ p^{-}_{\\dot x x} & p^{-}_{\\dot x \\dot x} \\end{bmatrix} \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix}}{\\begin{bmatrix} 1 & 0 \\end{bmatrix} \\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\dot x x} \\\\ p^{-}_{\\dot x x} & p^{-}_{\\dot x \\dot x} \\end{bmatrix} \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix} + \\begin{bmatrix} r_{xx} \\end{bmatrix}} = \\frac{\\begin{bmatrix} p^{-}_{xx} \\\\ p^{-}_{x \\dot x} \\end{bmatrix}}{p^{-}_{xx} + r_{xx}}$$\n",
"\n",
"Once again, the index $t$ has been dropped from the elements of $\\mathbf{P}^{-}_t$ for readability."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"The update equations for the position and speed become:\n",
"\n",
"- Position: $\\hat{x}_t = \\hat{x}_t^{-} + \\frac{p^{-}_{xx}}{p^{-}_{xx} + r_{xx}} \\Delta_t$\n",
"- Speed: $\\hat{\\dot x}_t = \\hat{\\dot x}_t^{-} + \\frac{p^{-}_{x \\dot x}}{p^{-}_{xx} + r_{xx}} \\Delta_t$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- One key observation is that the speed is updated even if we do not have a direct measure for it in our measurement $\\boldsymbol{z}_t$.\n",
"- The update is possible thanks to the term $p^{-}_{x \\dot x}$ in $\\mathbf{K}_t$ that relates the position (and its measurement) to the velocity.\n",
"- This showcases one of the main strengths of KF: it can handle *partial observations*."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Let's consider the two extreme cases for the values of $\\mathbf{K}_t$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"1. The measurement error covariance $\\mathbf{R}$ approaches zero, i.e., there is no noise in the measurements.\n",
" - The measurements are completely reliable.\n",
" - We have $\\lim \\limits_{\\mathbf{R} \\rightarrow 0} \\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T(\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1} = \\mathbf{H}^{-1}$\n",
" - $\\mathbf{K}_t$ weights the residuals more heavily: \n",
" \n",
" $$\n",
" \\begin{aligned} \n",
" \\hat{\\boldsymbol{x}}_{t} &= \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\Delta_t = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}(\\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t})\\\\ \n",
" &= \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\boldsymbol{z}_{t} - \\mathbf{H}^{-1}\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\boldsymbol{z}_{t} - \\hat{\\boldsymbol{x}}^{-}_{t}\\\\ \n",
" &= \\mathbf{H}^{-1}\\boldsymbol{z}_{t} \n",
" \\end{aligned}\n",
" $$\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"2. The covariance estimate $\\mathbf{P}^{-}_t$ approaches zero.\n",
" - The model is completely reliable and the measurements are not accounted for.\n",
" - We have $\\lim \\limits_{\\mathbf{P}^{-}_t \\rightarrow 0} \\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T(\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1} = 0$.\n",
" - $\\mathbf{K}_t$ does not give importance to the residuals: $\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + 0\\Delta_t = \\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- The last thing that remains to update is the posterior covariance estimate $\\mathbf{P}_t$ to be used in the next predict step.\n",
"- The formula is:\n",
"\n",
"$$\\mathbf{P}_t = (\\mathbf{I} - \\mathbf{K}_t \\mathbf{H})\\mathbf{P}^{-}_t$$\n",
"\n",
"- Let's look again at the two extreme cases."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"1. $\\mathbf{K}_t = \\mathbf{H}^{-1} \\rightarrow \\mathbf{P}_t = 0$\n",
" - This is the case where the measurements are completely reliable.\n",
" - The only source of uncertainty at the next Predict step will be the one of the model: $\\mathbf{P}_t^{-} = \\mathbf{A}\\mathbf{P}_{t-1}\\mathbf{A}^T + \\mathbf{Q} = \\mathbf{Q}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"2. $\\mathbf{K}_t = 0 \\rightarrow \\mathbf{P}_t = \\mathbf{P}^{-}_t$\n",
" - This is the case where the measurements are unreliable and not accounted for.\n",
" - The posterior covariance estimate is exactly the same as the prior covariance estimate $\\mathbf{P}^{-}_t$ obtained in the Predict step using the model and not modified with the new measurements in the Correct step."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Summary of the Correct step:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The whole process is summarized below.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Parameters estimation\n",
"\n",
"In general, both the measurement noise covariance $\\mathbf{R}$ and the process noise covariance $\\mathbf{Q}$ are **unknown**.\n",
"\n",
"- Estimating $\\mathbf{R}$ is usually done before starting to apply the filter. \n",
"- Measuring $\\mathbf{R}$ is usually possible because we measure the process anyway (while operating the filter).\n",
"- We can take some offline sample measurements to determine the variance of the measurement noise."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Determining the process noise covariance $\\mathbf{Q}$ is generally more difficult.\n",
"- We typically do not have the ability to directly observe the process we are estimating. \n",
"- Sometimes a relatively simple process model can produce acceptable results if one \"injects\" enough uncertainty into the process. \n",
"- This works well if the process measurements are reliable."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"### Extensions and Variants\n",
" - The Extended Kalman Filter (EKF) is used for nonlinear systems by linearizing about the current estimate.\n",
" - The Unscented Kalman Filter (UKF) uses a deterministic sampling technique to capture the mean and variance of the state distribution.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Practical Considerations\n",
"- Designing an accurate model and having reliable measurements are crucial for effective filtering.\n",
"- Dealing with nonlinearities can be challenging and may require the EKF or UKF.\n",
"- Tuning process and measurement noise covariances $\\mathbf{Q}$ and $\\mathbf{R}$ is essential for optimal performance.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Python implementation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Next, we will implement the Kalman Filter in Python and use it to estimate the value of a signal from noisy data. \n",
"- Initially, we will construct the algorithm by hand so we understand all the steps involved. \n",
"- Also, we will generate datasets from scratch so we know what our true result should be and therefore gain insight into how well the Kalman Filter works. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### The algorithm \n",
"\n",
"This is the workflow of the algorithm we are going to implement:\n",
"\n",
"1. Make an initial estimate of your state vector and covariance matrix.\n",
"2. Predict the state and covariance for the next time step.\n",
"3. Compute the Kalman gain.\n",
"4. Make a measurement.\n",
"5. Update estimates of state and covariance.\n",
"6. Repeat from step 2."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"For one-dimensional examples, there are a couple of simplifications: \n",
"- the state vector is a single number (a scalar).\n",
"- the covariance matrix reduces to the variance (also a scalar). \n",
"\n",
"Therefore, only normal multiplication (as opposed to matrix multiplication) is involved."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Example 1: Static one-dimensional data\n",
"\n",
"- Let's start by considering a simple example: determine the position of a stationary car. \n",
"- We'll assume that all we are interested in is a one-dimensional value, such as the distance along a road from a landmark.\n",
"- We are going to try to predict the true position based on some noisy measurements of the distance. \n",
"- Since we are going to generate the noisy measurements by hand, we will know the true position and we can see how well our algorithm performs. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We assume the measurements to follow this distribution:\n",
"\n",
"$$z_t = \\mu + v_t \\;\\; \\text{with}\\;\\; v_t \\sim \\mathcal{N}(0, R)$$ \n",
"\n",
"- where $\\mu$ is the actual position and $R$ is the actual measurement noise covariance.\n",
"- Note that both $\\mu$ and $R$ are unknown to the filter."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mu = 124.5 # Actual position\n",
"R = 0.1 # Actual standard deviation of actual measurements (R)\n",
"\n",
"# Generate measurements\n",
"n_measurements = 1000 # Change the number of points to see how the convergence changes\n",
"Z = np.random.normal(mu, np.sqrt(R), size=n_measurements)\n",
"\n",
"plt.plot(Z,'k+',label='measurements $z_t$',alpha=0.2)\n",
"plt.title('Noisy position measurements')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('$z_t$')\n",
"plt.tight_layout();"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- As discussed previously, since the actual measurement noise covariance is unknown, we have to provide an estimate.\n",
"- This is usually feasible as we can estimate $R$ from the available measurements.\n",
"- In addition, we need to estimate the process noise covariance $Q$, which is usually harder to guess.\n",
"- Remember that $Q$ represents the noise of the model used to describe the actual position.\n",
"- In our case:\n",
"\n",
"$$x_t = x_0 + w_t \\;\\; \\text{with}\\;\\; w_t \\sim \\mathcal{N}(0,Q)$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- In summary, we have two noise parameters: one for the model ($Q$) and one for the measurement ($R$). \n",
"- The values of $Q$ and $R$ are inputs for the algorithm. \n",
"- Therefore, since we usually don't know $Q$ and $R$, we have to make reasonable estimates. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- In this example, we know the actual variance for the measurements (since we are generating the measurements ourselves).\n",
"- We can see the effect of making a good or a bad estimate for this parameter. \n",
"- Also, we know the actual position of our car, so we can see how quickly the KF converges to the true value.\n",
"- Finally, we need to guess initial values for the initial position of the car and the variance in this initial estimate. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Estimated covariances\n",
"Q_est = 1e-4 \n",
"R_est = 2e-2 "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Create a function that computes the estimated position and associated error using the KF."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def kalman_1d(x, P, measurement, R_est, Q_est):\n",
" \n",
" # Prediction\n",
" x_pred = x\n",
" P_pred = P + Q_est\n",
"\n",
" # Update\n",
" K = P_pred / (P_pred + R_est)\n",
" x_est = x_pred + K * (measurement - x_pred)\n",
" P_est = (1 - K) * P_pred\n",
"\n",
" return x_est, P_est"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Apply the filter to the 1D static measurements."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# initial guesses \n",
"x = 123 # Use an integer (imagine the initial guess is determined with a meter stick)\n",
"P = 0.04 # error covariance P\n",
"\n",
"KF_estimate=[] # To store the position estimate at each time point \n",
"KF_error=[] # To store estimated error at each time point\n",
"for z in Z:\n",
" x, P = kalman_1d(x, P, z, R_est, Q_est)\n",
" KF_estimate.append(x)\n",
" KF_error.append(P)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Now, plot an estimate for the value $x_t$ on top of the measurements $z_t$. \n",
"- We also plot how the covariance estimate $P_t$ changes during the process. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "skip"
},
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"def plot_1d_comparison(measurements_made, estimate, true_value, axis):\n",
" axis.plot(measurements_made,'k+',label='measurements',alpha=0.3)\n",
" axis.plot(estimate,'-',label='KF estimate')\n",
" if not isinstance(true_value, (list, tuple, np.ndarray)):\n",
" # plot line for a constant value\n",
" axis.axhline(true_value,color='r',label='true value', alpha=0.5) \n",
" else:\n",
" # for a list, tuple or array, plot the points\n",
" axis.plot(true_value,color='r',label='true value', alpha=0.5)\n",
" axis.legend(loc = 'lower right')\n",
" axis.set_title('Estimated position vs. time step')\n",
" axis.set_xlabel('Time')\n",
" axis.set_ylabel('$x_t$')\n",
" \n",
"def plot_1d_error(estimated_error, lower_limit, upper_limit, axis):\n",
" # lower_limit and upper_limit are the lower and upper limits of the vertical axis \n",
" axis.plot(estimated_error, label='KF estimate for $P$')\n",
" axis.legend(loc = 'upper right')\n",
" axis.set_title('Estimated error vs. time step')\n",
" axis.set_xlabel('Time')\n",
" axis.set_ylabel('$P_t$')\n",
" plt.setp(axis,'ylim',[lower_limit, upper_limit])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1,2, figsize=(12, 5))\n",
"plot_1d_comparison(Z, KF_estimate, mu, axes[0])\n",
"plot_1d_error(KF_error, 0, 0.015, axes[1])\n",
"plt.tight_layout();"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We see that for the parameters we've chosen, the filter converges to the true value quickly and the noise is filtered out. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Question:** How much the KF estimate fluctuates around the true value? "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Answer:** The fluctuations around the true value are approximately the size of the standard deviation of the estimate, which is $\\sqrt{P_t}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example 2: Dynamic one-dimensional data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Let's now consider a car that moves with initial velocity $v_0$ and constant acceleration $a$.\n",
"- The position changes over time according to the following formula:\n",
"\n",
"$$x_{t} = x_{t-1} + \\delta t \\cdot v_0 + \\delta t^2 \\cdot a + w_t$$ \n",
"\n",
"- where $w_t \\sim \\mathcal{N}(0, Q)$ represents the model noise."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Now, assume we have measurements only for the position, but not the velocity:\n",
"$$z_t = x_t$$\n",
"- In practice, to simulate our measurements we use the following distribution:\n",
"$$z_t \\sim \\mathcal{N}(x_t, R)$$\n",
"- where $R$ represents the variance of the measurements error."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Let's now modify our static algorithm to take into account this motion. \n",
"- We build the real trajectory.\n",
"- We simulate measurements of the positions.\n",
"- Then we will apply the KF and compare the KF estimate for the position with both the actual position $x_t = x_0 + v_0 \\cdot t$ and the measured positions $z_t$."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# initial parameters\n",
"v0 = 0.3\n",
"a = 5e-3\n",
"x0 = 0.0 \n",
"R = 8.0\n",
"delta_t = 0.1\n",
"\n",
"# generate true positions\n",
"n_measurements = 1000\n",
"Xt = np.zeros(n_measurements) \n",
"for t in range(0, n_measurements):\n",
" Xt[t]= x0 + v0*t*delta_t + a*t**2*delta_t**2\n",
"\n",
"# generate noisy measurements\n",
"Zx = np.zeros(n_measurements)\n",
"for t in range(0, n_measurements):\n",
" Zx[t]= Xt[t] + np.random.normal(0, np.sqrt(R), 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(Zx,'k+',label='measurements $z_t$', alpha=0.2)\n",
"plt.title('Noisy position measurements')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('$z_t$')\n",
"plt.tight_layout();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Initial guesses and estimates\n",
"x = 0 \n",
"P = 0.5\n",
"Q_est = 4 \n",
"R_est = 4\n",
"KF_estimate = [] # Store the position estimate at each time step\n",
"KF_error = [] # Store estimated error at each time step\n",
"\n",
"# Kalman filter\n",
"for z in Zx:\n",
" x, P = kalman_1d(x, P, z, R_est, Q_est)\n",
" KF_estimate.append(x)\n",
" KF_error.append(P)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1,2, figsize=(12, 5))\n",
"plot_1d_comparison(Zx, KF_estimate, Xt, axes[0])\n",
"plot_1d_error(KF_error, min(KF_error)-0.1, max(KF_error)+0.1, axes[1])\n",
"plt.tight_layout();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The measurements are not close to the true value because the variance of the measurements error $R$ is large compared to the velocity. \n",
"- The KF estimate is tracking the measurements, so it oscillates around the true value. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```{note}\n",
"- With dynamic models, there are more parameters to tune, so it can be more challenging to reach convergence. \n",
"- If we had a way to measure velocity, we could use that information too. \n",
"- Nevertheless, the KF can work with incomplete observations, which is one of its main advantages.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example 3: Dynamic two-dimensional data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Now we consider an example that is closer to real-world applications: estimating the motion of a point in two dimensions, $x$ and $y$. \n",
"- This 2D algorithm is used in mouse tracking software and also to track objects in videos. \n",
"- As before, we will use instantaneous measurements for the position ($x, y$).\n",
"- We are going to use the Kalman filter built into OpenCV. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Our system is defined by the following quantities:\n",
"\n",
"- State vector $\\boldsymbol{x} = \\begin{bmatrix} x \\\\ y \\\\ \\dot x \\\\ \\dot y \\end{bmatrix}$, representing the 2D position and 2D velocity.\n",
"- Measurement vector $\\boldsymbol{z} = \\begin{bmatrix} z_x \\\\ z_y \\end{bmatrix}$, representing measurements for the 2D position."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Transition matrix $\\mathbf{A} = \\begin{bmatrix} 1 & 0 & \\delta_t & 0 \\\\ 0 & 1 & 0 & \\delta_t \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 0 & 0 & 1 \\end{bmatrix}$ (called `transitionMatrix` in OpenCV).\n",
"- Measurement matrix $\\mathbf{H} = \\begin{bmatrix} 1 & 0 & 0 & 0 \\\\ 0 & 1 & 0 & 0 \\end{bmatrix}$ (called `measurementMatrix` in OpenCV)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- The covariance of the model noise $\\mathbf{Q} = \\begin{bmatrix} q & 0 & 0 & 0 \\\\ 0 & q & 0 & 0 \\\\ 0 & 0 & q & 0 \\\\ 0 & 0 & 0 & q \\end{bmatrix}$ (called `processNoiseCov` in OpenCV).\n",
"- The covariance of the measurements noise $\\mathbf{R} = \\begin{bmatrix} r & 0 \\\\ 0 & r \\end{bmatrix}$ (called `measurementNoiseCov` in OpenCV).\n",
"\n",
"For simplicity, we use diagonal covariances in this example."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"The constructor has has the following sintax:\n",
"\n",
"> `KalmanFilter(state_size, measurements_size, control_size)`\n",
"\n",
"where:\n",
"- `state_size` is the dimension of the state vector $\\boldsymbol{x}$, which is 4 in our case.\n",
"- `measurements_size` is the dimension of the state vector $\\boldsymbol{x}$, which is 2 in our case.\n",
"- `control_size`, which is 0 since we do not have the optional control $\\boldsymbol{u}$ (not discussed in this lecture)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"kalman = cv2.KalmanFilter(4,2,0) # 4 states, 2 measurements, 0 control vector\n",
"\n",
"q = 1 # the variance in the model\n",
"r = 20 # the variance in the measurement\n",
"dtime = 1 # size of time step\n",
"\n",
"kalman.measurementMatrix = np.array([[1,0,0,0],\n",
" [0,1,0,0]],np.float32) # H\n",
"kalman.transitionMatrix = np.array([[1,0,dtime,0],\n",
" [0,1,0,dtime],\n",
" [0,0,1,0],\n",
" [0,0,0,1]],np.float32) # A\n",
"kalman.processNoiseCov = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1]],np.float32) * q # Q\n",
"kalman.measurementNoiseCov = np.array([[1,0],\n",
" [0,1]],np.float32) * r # R\n",
"\n",
"KF_estimate_xy = [] # To store the position estimate at each time point"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Next, we will load some pre-computed data."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"xy_motion = pd.read_csv('https://zenodo.org/records/10951538/files/kf_ts1.csv?download=1',\n",
" header = None).values.astype('float32')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Here, we use pre-computed data for simplicity/reproducibility.\n",
"- However, you can generate your own $(z_x, z_y)$ measurements by clicking first on the grid and then on ``Generate Sample``."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "-"
},
"tags": [
"hide-input"
]
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('https://guoguibing.github.io/librec/datagen.html', width=900, height=800)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- As always, take a look at the data before analyzing it. \n",
"- Also, it is good practice to make sure the data loaded properly."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"205\n",
"[[2.35 1.45]\n",
" [3.55 1.8 ]\n",
" [3. 2.9 ]\n",
" [2.65 4.2 ]\n",
" [2.95 5.15]\n",
" [3.7 5.4 ]\n",
" [4.8 5.55]\n",
" [5.3 5.25]\n",
" [5.7 5.25]\n",
" [5.8 5.85]]\n"
]
}
],
"source": [
"print(len(xy_motion))\n",
"print(xy_motion[0:10]) # Print first 10 items, but can look at all 205 or plot them"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We are now ready to apply the KF. \n",
"- In the OpenCV implementation of the KF there is no initialization options. \n",
"- Both the state vector $\\boldsymbol{x}$ and the covariance matrix $\\mathbf{P}$ are always initialized to zero."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"for i in xy_motion:\n",
" pred = kalman.predict() # predicts new state using the model\n",
" kalman.correct((i)) # updates estimated state with the measurement\n",
" KF_estimate_xy.append(((pred[0]),(pred[1]))) # store the estimated position"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"205\n"
]
}
],
"source": [
"# Quick check: estimate has same length as measurement data\n",
"print(len(KF_estimate_xy))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Now plot the data and the KF estimate."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_est, y_est = zip(*KF_estimate_xy)\n",
"x_true, y_true = zip(*xy_motion)\n",
"plt.scatter(x_est, y_est, marker= '.', label = 'KF estimate', alpha = 0.5)\n",
"plt.scatter(x_true, y_true,marker= '.', label = 'true value', alpha = 0.5)\n",
"plt.legend(loc = 'lower center')\n",
"plt.xlabel('x coordinate')\n",
"plt.ylabel('y coordinate');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Pretty good agreement. \n",
"- Once again, you are encouraged to change the parameters and explore what happens.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Summary\n",
"\n",
"What we covered in this lecture:\n",
"\n",
"1. What is the Kalman Filter\n",
"2. The recursive formulation used to update the predictions.\n",
"3. The importance of tuning the parameters of the Kalman Filter.\n",
"4. How to apply the Kalman Filter in various situations.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Exercise #1\n",
"This exercise refers to [Example 1](#example-1-static-one-dimensional-data).\n",
"1. Choose a value for the estimated model variance `Q_est` that is larger than estimated measurement variance `R_est`.\n",
"2. Repeat the analysis of Example 1 for this new value. \n",
"3. Does the KF estimate converge?\n",
"4. Why the estimates changed? Do they look more noisy than before? Why?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Exercise #2\n",
"\n",
"This exercise refers to [Example 2](#example-2-dynamic-one-dimensional-data). \n",
"\n",
"In the case we examined above, the KF estimate was close to the measurements and both were different from the true value.\n",
"Change the parameters of the algorithm until you find some combinations that achieve the following:\n",
"\n",
"1. Measurements, KF estimate and true value are all close.\n",
"2. Measurements, KF estimate and true value are all noticeably different.\n",
"3. The measurements are close to the true value, but the KF estimate is different.\n",
"\n",
"Discuss your findings."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Exercise #3\n",
"\n",
"This exercise refers to [Example 3](#example-3-dynamic-two-dimensional-data). \n",
"\n",
"In the original example, we used both Position and Velocity (PV model) in the state vector, i.e., $\\boldsymbol{x} = \\begin{bmatrix} x & y & \\dot x & \\dot y \\end{bmatrix}^T$. \n",
"\n",
"What happens if we use only the Position (P model) to describe the state? After all, our measurements only provide position. Do we really need to include the velocity?\n",
"\n",
"1. Rewrite the algorithm above for the P model. \n",
"\n",
"```{hint}\n",
"What is the size of the state vector in this case? What are the dimensions of the matrices that characterize the system?\n",
"```"
]
}
],
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"language": "python",
"name": "python3"
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![](\"media/cover.png\")
"
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"source": [
"## Overview\n",
"\n",
"This lecture will cover the following topics:\n",
"- Introduction to the Kalman Filter.\n",
"- Model components and assumptions.\n",
"- The Kalman Filter algorithm.\n",
"- Application to static and dynamic one-dimensional data.\n",
"- Application to higher-dimensional data."
]
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"# Imports\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import cv2\n",
"np.random.seed(0) # for reproducibility"
]
},
{
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"metadata": {},
"source": [
"---"
]
},
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"cell_type": "markdown",
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"slide_type": "slide"
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"source": [
"## What is a Kalman Filter?"
]
},
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"- The Kalman Filter (KF) is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies.\n",
"- It produces estimates of unknown variables that tend to be more accurate than those based only on measurements.\n",
"- Developed by Rudolf E. Kálmán in the late 1950s.\n",
"- Applications:\n",
" - tracking objects (Apollo project, GPS, self driving cars).\n",
" - image processing.\n",
" - economic and financial modeling."
]
},
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"source": [
"### Theoretical foundations\n",
"\n",
"- Dynamic systems are systems that change over time.\n",
"- They are described by a set of equations that predict the future state of the system based on its current state.\n",
"- The KF assumes the system is a *linear* dynamic system with *Gaussian* noise. \n",
"- Gaussian distributions are used because of their nice properties when dealing with averages and variances."
]
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"### What are the ingredients?\n",
"\n",
"**State** \n",
"- The true value of the variables we want to estimate. \n",
"- The state vector represents all the information needed to describe the current state of the system.\n",
"\n",
"**Observation model** \n",
"- Relates the current state to the measurements or observations."
]
},
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"slide_type": "subslide"
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"**Noisy measurements** \n",
"- The process noise and measurement noise (assumed to be Gaussian) represent the uncertainty in our models and measurements. "
]
},
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "subslide"
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"source": [
"Example:\n",
"- Car moving at a constant velocity.\n",
"- Model: Car position = velocity $\\times$ time $+$ system noise.\n",
"- Measurements: position and velocity (which are also noisy)."
]
},
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"cell_type": "markdown",
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"source": [
"### The role of noise\n",
"\n",
"The tradeoff between the influence of the model and the measurements is determined by noise.\n",
"\n",
"- If the model has relatively large errors, more importance is given to the latest measurements in computing the current estimate.\n",
"- If the measurement have larger errors, more importance is given to the model in making the current estimate.\n",
"- Therefore, you need to estimate not only your state but also the errors (the covariance) for both the model and the measurements.\n",
"- These must be updated at each time step, too."
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"### Update strategy\n",
"\n",
"KF is a recursive algorithm:\n",
"\n",
"- Uses information from previous time step to update the estimates.\n",
"- Does not keep in memory all the data acquired so far.\n",
"- Has predictor-corrector structure:\n",
" - Make a prediction based on the model.\n",
" - Update the prediction with the measurements.\n",
" - Repeat."
]
},
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"### Fundamental assumptions\n",
"\n",
"The Kalman filter approach is based on three fundamental assumptions:\n",
"\n",
"1. The system can be described or approximated by a linear model.\n",
"2. All noise (from both the system and the measurements) is white, i.e., the values are not correlated.\n",
"3. All noise is Gaussian."
]
},
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"slide_type": "subslide"
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"source": [
"#### Assumption 1: linearity\n",
"\n",
"Each variable at the current time is a linear function of the variables at previous times.\n",
"\n",
"- Many systems can be approximated this way.\n",
"- Linear systems are easy to analyze.\n",
"- Nonlinear systems can often be approximated by linear models around a current estimate (extended KF)."
]
},
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"slideshow": {
"slide_type": "subslide"
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"source": [
"#### Assumption 2: Whiteness\n",
"\n",
"The noise values are not correlated in time.\n",
"\n",
"- If you know the noise at time $t$, it doesn't help you to predict the noise at future times $t+\\tau$.\n",
"- White noise is a reasonable approximation of the real noise.\n",
"- The assumption makes the mathematics tractable."
]
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"source": [
"```{note}\n",
"White noise contains a mix of all the different frequencies at the same intensity blended together. \n",
"\n",
"This is similar to how white light contains all the colors of the rainbow combined.\n",
"```"
]
},
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"slide_type": "subslide"
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"source": [
"#### Assumption 3: Gaussian noise\n",
"\n",
"At any point in time, the probability density of the noise is a Gaussian.\n",
"\n",
"- System and measurement noise are often a combination of many small sources of noise. \n",
"- The combination effect is approximately Gaussian.\n",
"- If only mean and variance are known (typical case in engineering systems), Gaussian distribution is a good choice as these two quantities completely determine the Gaussian distribution.\n",
"- Gaussian distribution have nice properties and are easy to treat mathematically.\n",
"\n",
"---"
]
},
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"source": [
"## Technical details"
]
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"source": [
"### Combining two sources\n",
"\n",
"Assume a car has a certain initial position $x_0=0$ and initial velocity $\\dot{x}_0=60 km/h$.\n",
"\n",
"If the speed is constant, we have:\n",
"\n",
"$$x_{t} = 1\\cdot x_{t-1} + \\delta t \\cdot \\dot{x}_{t-1}$$\n",
"$$\\dot{x}_{t} = 0 \\cdot x_{t-1} + 1 \\cdot \\dot{x}_{t-1}$$\n"
]
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"In matrix form:\n",
"\n",
"$$ \\boldsymbol{x}_{t} = \n",
"\\begin{bmatrix}\n",
"x_{t} \\\\\n",
"\\dot{x}_{t}\n",
"\\end{bmatrix}\n",
"=\n",
"\\begin{bmatrix}\n",
"1 & \\delta t \\\\\n",
"0 & 1 \n",
"\\end{bmatrix}\n",
"\\begin{bmatrix}\n",
"x_{t-1} \\\\\n",
"\\dot{x}_{t-1}\n",
"\\end{bmatrix} = \\mathbf{A} \\boldsymbol{x}_{t-1}\n",
"$$\n",
"\n",
"![](\"media/system.png\")
"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"- The current position and speed will evolve according to this simple dynamical system \n",
"\n",
"$$\\boldsymbol{x}_{t} = \\mathbf{A} \\boldsymbol{x}_{t-1} + \\boldsymbol{w}_{t-1}$$ \n",
"\n",
"- where $\\boldsymbol{w}_t \\sim \\mathcal{N}(0,\\mathbf{Q})$ represents perturbations in the underlying dynamical systems (e.g., holes on the road).\n",
"- $\\boldsymbol{x}_{t} \\in \\mathbb{R}^{N}$ represents the state of the system.\n",
"- $\\mathbf{Q} \\in \\mathbb{R}^{N \\times N}$ is the *process noise covariance*, where $N$ represents the number of state variables (2 in our case, position and speed)."
]
},
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"source": [
"Where the car will be after 1 minute?\n",
"\n",
"If we rely only on our simple sistem $\\boldsymbol{x}_{t+1} = \\mathbf{A} \\boldsymbol{x}_{t}$ without even consider the noise, the car will keep moving at $60 km/h$ and will be at $1 km$ distance.\n",
"\n",
"![](\"media/model_pred.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"Let say our car has a GPS.\n",
"- As all instruments, our GPS will be affected by errors and gives us measurements with uncertainties in it.\n",
"- In addition, our GPS can only measure the position but not the speed.\n",
"- Based on our GPS, after 1 minute we are at $0.8 km$ from where we started.\n",
"\n",
"![](\"media/gps.png\")
"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"- Let the measure of our GPS be \n",
"\n",
"$$\\boldsymbol{z}_t = \\mathbf{H} \\boldsymbol{x}_t + \\boldsymbol{v}_t$$ \n",
"\n",
"- where $\\boldsymbol{v}_t \\sim \\mathcal{N}(0,\\mathbf{R})$ represents the uncertainty in the measures.\n",
"- $\\boldsymbol{z}_{t} \\in \\mathbb{R}^{M}$ is the measurements vector.\n",
"- In our case, since the GPS measures only the position, we have $\\mathbf{H} = \\begin{bmatrix} 1 & 0 \\end{bmatrix}$ and $\\mathbf{R} = \\begin{bmatrix} r_{xx} \\end{bmatrix}$.\n",
"- In general, $\\mathbf{H} \\in \\mathbb{R}^{M,N}$ is a matrix that maps the $N$ state variables into the $M$ measurements and $\\mathbf{R} \\in \\mathbb{R}^{M,M}$ is the measurement noise covariance."
]
},
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"In summary, we have two different sources of information:\n",
"- a linear stochastic difference equation, representing our imprecise knowledge of a discrete-time controlled process;\n",
"- a source of measurements, that are noisy.\n",
"\n",
"![](\"media/inputs.png\")
\n",
"\n",
"How do we combine them to get the best possible estimate of our system variables?"
]
},
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"### The KF algorithm\n",
"\n",
"The KF is an interative procedure that consists of two steps: \n",
"\n",
"- **Predict** (Time update)\n",
"- **Correct** (Measurement update)\n",
"\n",
"These two steps are used to update two quantities:\n",
"- The **state estimate**.\n",
"- The uncertainty about our state estimate, called **covariance estimate**.\n",
"\n",
"Before looking at the iterative procedure let's introduce this second quantity."
]
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"**Covariance estimate**\n",
"\n",
"There are two sources of error in the estimate of the state of our system.\n",
"\n",
"- The *prior* estimate error $\\boldsymbol{e}^{-}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}^{-}_t$.\n",
"- The *posterior* estimate error $\\boldsymbol{e}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}_t$.\n",
"\n",
"Where:\n",
"- $\\hat{\\boldsymbol{x}}^{-}_t$ is the estimate of the state based only on the knowledge of the system, e.g., the dynamics equations.\n",
"- $\\hat{\\boldsymbol{x}}_t$ is the estimate based also on the measurement $\\boldsymbol{z}_t$, e.g., the GPS. "
]
},
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"Each type of error is associated with a covariance matrix, which reflects the amount of uncertainty in the state estimates.\n",
"\n",
"- $\\mathbf{P}_t^{-} = \\mathbb{E}[\\boldsymbol{e}^{-}_t \\boldsymbol{e}^{-T}_t] \\in \\mathbb{R}^{N\\times N}$ (*prior* covariance estimate).\n",
"- $\\mathbf{P}_t = \\mathbb{E}[\\boldsymbol{e}_t \\boldsymbol{e}^{T}_t] \\in \\mathbb{R}^{N\\times N}$ (*posterior* covariance estimate).\n",
"- In our case, we have $\\mathbf{P}_t = \\begin{bmatrix} p_{xx} & p_{\\dot x x} \\\\ p_{\\dot x x} & p_{\\dot x \\dot x} \\end{bmatrix}$ (for readability, the index $t$ on the matrix elements is omitted):\n",
" - $p_{xx}$ is the uncertainty about the position;\n",
" - $p_{\\dot x \\dot x}$ is the uncertainty about the velocity;\n",
" - $p_{x \\dot x}$ and $p_{\\dot x x}$ represent the correlation between the noise in the measurements of the position and the speed.\n",
"- $\\mathbf{P}_t^{-}$ has the same form."
]
},
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"slide_type": "slide"
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"#### Predict\n",
"\n",
"- The dynamics equations are responsible for projecting forward (in time) the current state and error covariance estimates to obtain the *prior* estimate for the next time step.\n",
"- Such time update equations can also be thought of as *predictor equations*.\n",
"\n",
"State estimate update: \n",
"\n",
"$$\\hat{\\boldsymbol{x}}_{t}^{-} = \\mathbf{A} \\hat{\\boldsymbol{x}}_{t-1}$$\n",
"\n",
"![](\"media/update.png\")
"
]
},
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "slide"
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"#### Correct\n",
"\n",
"The measurement update equations are responsible for the feedback, i.e. for incorporating a new measurement into the prior estimate to obtain an improved posterior estimate.\n",
"\n",
"The posterior estimate $\\hat{\\boldsymbol{x}}_{t}$ is a linear combination of:\n",
"- The prior estimate $\\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"- A weighted difference between the actual measurement $\\boldsymbol{z}_{t}$ and a measurement prediction $\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"\n",
"$$\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{K}_t(\\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t})$$"
]
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "subslide"
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"source": [
"- The difference $\\Delta_t = \\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$ is called measurement *innovation* or *residual*.\n",
"- $\\Delta_t$ reflects the difference between our imprecise prediction ($\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t}$) and the actual noisy measurement ($\\boldsymbol{z}_{t}$).\n",
"- A residual of zero means that the two are in complete agreement.\n",
"\n",
"![](\"media/innovation.png\")
"
]
},
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"The matrix $\\mathbf{K} \\in \\mathbb{R}^{N \\times M}$ is chosen to be the **gain** that minimizes the posterior error covariance.\n",
"\n",
"Derivation (sketch):\n",
"1. Define the posterior error $\\boldsymbol{e}_t = \\boldsymbol{x}_t - \\hat{\\boldsymbol{x}}_t$.\n",
"2. Define the posterior error covariance $P_t = \\mathbb{E}[\\boldsymbol{e}_t \\boldsymbol{e}^{T}_t]$.\n",
"3. Substitute $\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{K}\\Delta_t$ in 1).\n",
"4. Substitute the $e_t$ obtained from 3) in 2) and take the expectation.\n",
"5. Take the derivative of the trace of the result with respect to $\\mathbf{K}$.\n",
"6. Set the result equal to zero.\n",
"7. Solve for $\\mathbf{K}$."
]
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"The previous derivation give us the following result:\n",
"\n",
"$$\\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T (\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1}$$\n",
"\n",
"Back to our car example, we have:\n",
"\n",
"$$\\mathbf{K}_t = \\frac{\\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\dot x x} \\\\ p^{-}_{\\dot x x} & p^{-}_{\\dot x \\dot x} \\end{bmatrix} \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix}}{\\begin{bmatrix} 1 & 0 \\end{bmatrix} \\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\dot x x} \\\\ p^{-}_{\\dot x x} & p^{-}_{\\dot x \\dot x} \\end{bmatrix} \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix} + \\begin{bmatrix} r_{xx} \\end{bmatrix}} = \\frac{\\begin{bmatrix} p^{-}_{xx} \\\\ p^{-}_{x \\dot x} \\end{bmatrix}}{p^{-}_{xx} + r_{xx}}$$\n",
"\n",
"Once again, the index $t$ has been dropped from the elements of $\\mathbf{P}^{-}_t$ for readability."
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"The update equations for the position and speed become:\n",
"\n",
"- Position: $\\hat{x}_t = \\hat{x}_t^{-} + \\frac{p^{-}_{xx}}{p^{-}_{xx} + r_{xx}} \\Delta_t$\n",
"- Speed: $\\hat{\\dot x}_t = \\hat{\\dot x}_t^{-} + \\frac{p^{-}_{x \\dot x}}{p^{-}_{xx} + r_{xx}} \\Delta_t$"
]
},
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "fragment"
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"- One key observation is that the speed is updated even if we do not have a direct measure for it in our measurement $\\boldsymbol{z}_t$.\n",
"- The update is possible thanks to the term $p^{-}_{x \\dot x}$ in $\\mathbf{K}_t$ that relates the position (and its measurement) to the velocity.\n",
"- This showcases one of the main strengths of KF: it can handle *partial observations*."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"source": [
"Let's consider the two extreme cases for the values of $\\mathbf{K}_t$."
]
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"1. The measurement error covariance $\\mathbf{R}$ approaches zero, i.e., there is no noise in the measurements.\n",
" - The measurements are completely reliable.\n",
" - We have $\\lim \\limits_{\\mathbf{R} \\rightarrow 0} \\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T(\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1} = \\mathbf{H}^{-1}$\n",
" - $\\mathbf{K}_t$ weights the residuals more heavily: \n",
" \n",
" $$\n",
" \\begin{aligned} \n",
" \\hat{\\boldsymbol{x}}_{t} &= \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\Delta_t = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}(\\boldsymbol{z}_{t} - \\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t})\\\\ \n",
" &= \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\boldsymbol{z}_{t} - \\mathbf{H}^{-1}\\mathbf{H} \\hat{\\boldsymbol{x}}^{-}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + \\mathbf{H}^{-1}\\boldsymbol{z}_{t} - \\hat{\\boldsymbol{x}}^{-}_{t}\\\\ \n",
" &= \\mathbf{H}^{-1}\\boldsymbol{z}_{t} \n",
" \\end{aligned}\n",
" $$\n",
"\n",
"![](\"media/extreme1.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
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"source": [
"2. The covariance estimate $\\mathbf{P}^{-}_t$ approaches zero.\n",
" - The model is completely reliable and the measurements are not accounted for.\n",
" - We have $\\lim \\limits_{\\mathbf{P}^{-}_t \\rightarrow 0} \\mathbf{K}_t = \\mathbf{P}^{-}_t \\mathbf{H}^T(\\mathbf{H} \\mathbf{P}^{-}_t \\mathbf{H}^T + \\mathbf{R})^{-1} = 0$.\n",
" - $\\mathbf{K}_t$ does not give importance to the residuals: $\\hat{\\boldsymbol{x}}_{t} = \\hat{\\boldsymbol{x}}^{-}_{t} + 0\\Delta_t = \\hat{\\boldsymbol{x}}^{-}_{t}$.\n",
"\n",
"![](\"media/extreme2.png\")
"
]
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "subslide"
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"- The last thing that remains to update is the posterior covariance estimate $\\mathbf{P}_t$ to be used in the next predict step.\n",
"- The formula is:\n",
"\n",
"$$\\mathbf{P}_t = (\\mathbf{I} - \\mathbf{K}_t \\mathbf{H})\\mathbf{P}^{-}_t$$\n",
"\n",
"- Let's look again at the two extreme cases."
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"1. $\\mathbf{K}_t = \\mathbf{H}^{-1} \\rightarrow \\mathbf{P}_t = 0$\n",
" - This is the case where the measurements are completely reliable.\n",
" - The only source of uncertainty at the next Predict step will be the one of the model: $\\mathbf{P}_t^{-} = \\mathbf{A}\\mathbf{P}_{t-1}\\mathbf{A}^T + \\mathbf{Q} = \\mathbf{Q}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"2. $\\mathbf{K}_t = 0 \\rightarrow \\mathbf{P}_t = \\mathbf{P}^{-}_t$\n",
" - This is the case where the measurements are unreliable and not accounted for.\n",
" - The posterior covariance estimate is exactly the same as the prior covariance estimate $\\mathbf{P}^{-}_t$ obtained in the Predict step using the model and not modified with the new measurements in the Correct step."
]
},
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"cell_type": "markdown",
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"slideshow": {
"slide_type": "subslide"
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"source": [
"Summary of the Correct step:\n",
"\n",
"![](\"media/correct.png\")
"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The whole process is summarized below.\n",
"\n",
"![](\"media/kalman.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Parameters estimation\n",
"\n",
"In general, both the measurement noise covariance $\\mathbf{R}$ and the process noise covariance $\\mathbf{Q}$ are **unknown**.\n",
"\n",
"- Estimating $\\mathbf{R}$ is usually done before starting to apply the filter. \n",
"- Measuring $\\mathbf{R}$ is usually possible because we measure the process anyway (while operating the filter).\n",
"- We can take some offline sample measurements to determine the variance of the measurement noise."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Determining the process noise covariance $\\mathbf{Q}$ is generally more difficult.\n",
"- We typically do not have the ability to directly observe the process we are estimating. \n",
"- Sometimes a relatively simple process model can produce acceptable results if one \"injects\" enough uncertainty into the process. \n",
"- This works well if the process measurements are reliable."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"### Extensions and Variants\n",
" - The Extended Kalman Filter (EKF) is used for nonlinear systems by linearizing about the current estimate.\n",
" - The Unscented Kalman Filter (UKF) uses a deterministic sampling technique to capture the mean and variance of the state distribution.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Practical Considerations\n",
"- Designing an accurate model and having reliable measurements are crucial for effective filtering.\n",
"- Dealing with nonlinearities can be challenging and may require the EKF or UKF.\n",
"- Tuning process and measurement noise covariances $\\mathbf{Q}$ and $\\mathbf{R}$ is essential for optimal performance.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Python implementation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Next, we will implement the Kalman Filter in Python and use it to estimate the value of a signal from noisy data. \n",
"- Initially, we will construct the algorithm by hand so we understand all the steps involved. \n",
"- Also, we will generate datasets from scratch so we know what our true result should be and therefore gain insight into how well the Kalman Filter works. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### The algorithm \n",
"\n",
"This is the workflow of the algorithm we are going to implement:\n",
"\n",
"1. Make an initial estimate of your state vector and covariance matrix.\n",
"2. Predict the state and covariance for the next time step.\n",
"3. Compute the Kalman gain.\n",
"4. Make a measurement.\n",
"5. Update estimates of state and covariance.\n",
"6. Repeat from step 2."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"For one-dimensional examples, there are a couple of simplifications: \n",
"- the state vector is a single number (a scalar).\n",
"- the covariance matrix reduces to the variance (also a scalar). \n",
"\n",
"Therefore, only normal multiplication (as opposed to matrix multiplication) is involved."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Example 1: Static one-dimensional data\n",
"\n",
"- Let's start by considering a simple example: determine the position of a stationary car. \n",
"- We'll assume that all we are interested in is a one-dimensional value, such as the distance along a road from a landmark.\n",
"- We are going to try to predict the true position based on some noisy measurements of the distance. \n",
"- Since we are going to generate the noisy measurements by hand, we will know the true position and we can see how well our algorithm performs. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We assume the measurements to follow this distribution:\n",
"\n",
"$$z_t = \\mu + v_t \\;\\; \\text{with}\\;\\; v_t \\sim \\mathcal{N}(0, R)$$ \n",
"\n",
"- where $\\mu$ is the actual position and $R$ is the actual measurement noise covariance.\n",
"- Note that both $\\mu$ and $R$ are unknown to the filter."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
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",
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