From a27ebac336ba21fba1f7ed59b4ec24fff62b27d0 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Sun, 7 Dec 2014 19:44:42 -0800 Subject: [PATCH] Added Ensemble Kalman Filter chapter. Had to rename and move directories around to allow the new chapter to fit in. --- .../Ensemble_Kalman_Filter.ipynb | 514 ++++++++++++++++++ .../Designing_Nonlinear_Kalman_Filters.ipynb | 0 .../HInfinity_Filters.ipynb | 0 README.md | 25 +- exp/1dposvel.ipynb | 415 ++++++++++++++ table_of_contents.ipynb | 23 +- 6 files changed, 955 insertions(+), 22 deletions(-) create mode 100644 11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter.ipynb rename {11_Designing_Nonlinear_Kalman_Filters => 12_Designing_Nonlinear_Kalman_Filters}/Designing_Nonlinear_Kalman_Filters.ipynb (100%) rename {12_HInfinity_Filters => 13_HInfinity_Filters}/HInfinity_Filters.ipynb (100%) create mode 100644 exp/1dposvel.ipynb diff --git a/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter.ipynb b/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter.ipynb new file mode 100644 index 0000000..78fa5cf --- /dev/null +++ b/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter.ipynb @@ -0,0 +1,514 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:89143715003512c2e724530f782eafab4fe0ced8e56da4ce0c97b2064b321ad3" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Ensemble Kalman Filters" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#format the book\n", + "%matplotlib inline\n", + "from __future__ import division, print_function\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.insert(0,'../code') # allow us to format the book\n", + "import book_format\n", + "book_format.load_style()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "\n", + "\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I am not well versed with Ensemble filters. I have implemented one for this book, and made it work, but I have not used one in real life. Different sources use slightly different forms of these equations. If I implement the equations given in the sources the filter does not work. It is possible that I am doing something wrong. However, in various places on the web I have seen comments by people stating that they do the kinds of things I have done in my filter to make it work. In short, I do not understand this topic well, but choose to present my lack of knowledge rather than to gloss over the subject. I hope to master this topic in the future and to author a more definitive chapter. At the end of the chapter I document my current confusion and questions. In any case if I got confused by the sources perhaps you will to, so documenting my confusion can help you avoid the same.\n", + "\n", + "The ensemble Kalman filter (EnKF) is very similar to the unscented Kalman filter (UKF) of the last chapter. If you recall, the UKF uses a set of deterministically chosen weighted sigma points passed through nonlinear state and measurement functions. After the sigma points are passed through the function, we find the mean and covariance of the points and use this as the filter's new mean and covariance. It is only an approximation of the true value, and thus suboptimal, but in practice the filter is highly accurate. It has the advantage of often producing more accurate estimates than the EKF does, and also does not require you to analytically derive the linearization of the state and measurement equations. \n", + "\n", + "The ensemble Kalman filter works in a similar way, except it uses a *Monte Carlo* method to choose the sigma points. The filter starts by randomly generating a large number of points distributed about the filter's initial state. This distribution is proportional to the filter's covariance $\\mathbf{P}$. In other words 68% of the points will be within one standard deviation of the mean, 95% percent within two standard deviations, and so on. Let's look at this in two dimensions. We will use `numpy.random.multivariate_normal()` function to randomly create points from a multivariate normal distribution drawn from the mean (5, 3) with the covariance\n", + "\n", + "$$\\begin{bmatrix}\n", + "32 & 15 \\\\ 15 & 40\n", + "\\end{bmatrix}$$\n", + "\n", + "I've drawn the covariance ellipse representing two standard deviations to illustrate how the points are distributed." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "from numpy.random import multivariate_normal\n", + "from stats import (covariance_ellipse, \n", + " plot_covariance_ellipse)\n", + "\n", + "mean = (5, 3)\n", + "P = np.array([[32, 15],\n", + " [15., 40.]])\n", + "\n", + "x,y = multivariate_normal(mean=mean, cov=P, size=2500).T\n", + "plt.scatter(x, y, alpha=0.3, marker='.')\n", + "plt.axis('equal')\n", + "\n", + "plot_covariance_ellipse(mean=mean, cov=p,\n", + " variance=2.**2,\n", + " facecolor='none')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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yxUxGWL48yWc+050Jsp5njrVtE4I7O53M1InDh21efz3BwYNOpvobtQZEUx3i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+ "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Algorithm\n", + "\n", + "As I already stated, when the filter is initialized a large number of sigma points are drawn from the initial state ($\\mathbf{x}) and covariance ($\\mathbf{P}$. From there the algorithm proceeds very similarly to the UKF. During the prediction step the sigma points are passed through the state transition function, and then perturbed by adding a bit of noise to account for the process noise. During the update step the sigma points are translated into measurement space by passing them through the measurement function, they are perturbed by a small amount to account for the measurement noise. The Kalman gain is computed from the \n", + "\n", + "We already mentioned the main difference between the UKF and EnKF - the UKF choses the sigma points deterministically. There is another difference, implied by the algorithm above. With the UKF we generate new sigma points during each predict step, and after passing the points through the nonlinear function we reconstitute them into a mean and covariance by using the *unscented transform*. The EnKF just keeps propagating the originally created sigma points; we only need to compute a mean and covariance as outputs for the filter! \n", + "\n", + "Let's look at the equations for the filter. As usual, I will leave out the typical subscripts and superscripts; I am expressing an algorithm, not mathematical functions. Here $N$ is the number of sigma points, $\\chi$ is the set of sigma points.\n", + "\n", + "**Initialize Step**\n", + "\n", + "$$\\chi \\sim \\mathcal{N}(\\mathbf{x}_0, \\mathbf{P}_0)\n", + "$$\n", + "\n", + "This just says to select the sigma points from the filter's initial mean and covariance. In code this might look like\n", + "\n", + " N = 1000\n", + " sigmas = multivariate_normal(mean=x, cov=P, size=N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Predict Step**\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\chi &= f(\\chi, u) + v_Q \\\\\n", + "x &= \\frac{1}{N} \\sum_1^N \\chi\n", + "\\end{aligned}\n", + "$$\n", + "That is short and sweet, but perhaps not entirely clear. The first line passes all of the sigma points through a use supplied state transition function and then adds some noise distributed according to the $\\mathbf{Q}$ matrix. In Python we might write" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " for i, s in enumerate(sigmas):\n", + " sigmas[i] = fx(x=s, dt=0.1, u=0.)\n", + "\n", + " sigmas += multivariate_normal(x, Q, N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second line computes the mean from the sigmas. In Python we will take advantage of `numpy.mean` to do this very concisely and quickly.\n", + "\n", + " x = np.mean(sigmas, axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now optionally compute the covariance of the mean. The algorithm does not need to compute this value, but it is often useful for analysis. The equation is\n", + "\n", + "$$\\mathbf{P} = \\frac{1}{N-1}\\sum_1^N[\\chi-\\mathbf{x}^-][\\chi-\\mathbf{x}^-]^\\mathsf{T}$$\n", + "\n", + "$\\chi-\\mathbf{x}^-$ is a one dimensional vector, so we will use `numpy.outer` to compute the $[\\chi-\\mathbf{x}^-][\\chi-\\mathbf{x}^-]^\\mathsf{T}$ term. In Python we might write\n", + "\n", + " P = 0\n", + " for s in sigmas:\n", + " P += outer(s-x, s-x)\n", + " P = P / (N-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Update Step**\n", + "\n", + "In the update step we pass the sigma points through the measurement function, compute the mean and covariance of the sigma points, compute the Kalman gain from the covariance, and then update the Kalman state by scaling the residual by the Kalman gain. The equations are\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\chi_h &= h(\\chi, u)\\\\\n", + "z_{mean} &= \\frac{1}{N}\\sum_1^N \\chi_h \\\\ \\\\\n", + "P_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\chi_h - z_{mean}][\\chi_h - z_{mean}]^\\mathsf{T} + R \\\\\n", + "P_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\chi - x^-][\\chi_h - z_{mean}]^\\mathsf{T} \\\\\n", + "\\\\\n", + "K &= P_{xz} P_{zz}^{-1}\\\\ \n", + "x & = x + K[z - v_R + \\chi_h]\n", + "\\end{aligned}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is very similar to the linear KF and the UKF. Let's just go line by line.\n", + "\n", + "The first line,\n", + "\n", + "$$\\chi_h = h(\\chi, u),$$\n", + "\n", + "just passes the sigma points through the measurement function $h$. We name the resulting points $\\chi_h$ to distinguish them from the sigma points. In Python we could write this as\n", + "\n", + " sigmas_h = h(sigmas, u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next line computes the mean of the measurement sigmas.\n", + "$$z_{mean} = \\frac{1}{N}\\sum_1^N \\chi_h$$\n", + "\n", + "In Python we can write\n", + "\n", + " z_mean = np.mean(sigmas_h, axis=0)\n", + " \n", + "Now that we have the mean of the measurement sigmas we can compute the covariance for every measurement sigma point, and the *cross variance* for the measurement sigma points vs the sigma points. That is expressed by these two equations\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "P_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\chi_h - z_{mean}][\\chi_h - z_{mean}]^\\mathsf{T} + R \\\\\n", + "P_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\chi - x^-][\\chi_h - z_{mean}]^\\mathsf{T}\n", + "\\end{aligned}$$\n", + "\n", + "We can express this in Python with\n", + "\n", + " P_zz = 0\n", + " for sigma in sigmas_h:\n", + " s = sigma - z_mean\n", + " P_zz += outer(s, s)\n", + " P_zz = P_zz / (N-1) + R\n", + "\n", + " P_xz = 0\n", + " for i in range(N):\n", + " P_xz += outer(self.sigmas[i] - self.x, sigmas_h[i] - z_mean)\n", + " P_xz /= N-1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computation of the Kalman gain is straightforward $K = P_{xz} P_{zz}^{-1}$.\n", + "\n", + "In Python this is the trivial\n", + "\n", + " K = np.dot(P_xz, inv(P_zz))\n", + "\n", + "Finally, we compute the new state \n", + "x & = x + K[z - v_R + \\chi_h]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outstanding Questions\n", + "\n", + "All of this should be considered as *my* questions, not lingering questions in the literature. However, I am copying equations directly from well known sources in the field, and they do not address the discrepencies.\n", + "\n", + "First, in Brown [1] we have all sums multipied by $\\frac{1}{N}$, as in \n", + "\n", + "$$ \\hat{x} = \\frac{1}{N}\\sum_{i=1}^N\\chi_k^{(i)}$$\n", + "\n", + "The same equation in Crassidis [2] reads (I'll use the same notation as in Brown, although Crassidis' is different)\n", + "\n", + "$$ \\hat{x} = \\frac{1}{N}\\sum_{i=1}^N\\chi_k^{(i)}$$\n", + "\n", + "The same is true in both sources for the sums in the computation for the covariances. Crassidis, in the context of talking about the filter's covariance, states that $N-1$ is used to ensure an unbiased estimate. Given the following standard equations for the mean and standard deviation (p.2 of Crassidis), this makes sense for the covariance.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mu &= \\frac{1}{N}\\sum_{i=1}^N[\\tilde{z}(t_i) - \\hat{z}(t_i)] \\\\\n", + " \\sigma^2 &= \\frac{1}{N-1}\\sum_{i=1}^N\\{[\\tilde{z}(t_i) - \\hat{z}(t_i)] - \\mu\\}^2\n", + "\\end{aligned}\n", + " $$\n", + " \n", + "However, I see no justification or reason to use $N-1$ to compute the mean. If I use $N-1$ in the filter for the mean the filter does not converge and the state essentially follows the measurements without any filtering. However, I do see a reason to use it for the covariance as in Crassidis, in contrast to Brown. Again, I support my decision emperically - $N-1$ works in the implementation of the filter, $N$ does not.\n", + "\n", + "My second question relates to the use of the $\\mathbf{R}$ matrix. In Brown $\\mathbf{R}$ is added to $\\mathbf{P}_{zz}$ whereas it isn't in Crassidis and other sources. I have read on the web notes by other implementers tthat adding R helps the filter, and it certainly seems reasonable and necessary to me, so this is what I do. \n", + "\n", + "My third question relates to the computation of the covariance $\\mathbf{P}$. Again, we have different equations in Crassidis and Brown. I have chosen the implementation given in Brown as it seems to give me the behavior that I expect (convergence of $\\mathbf{P}$ over time) and it closely compares to the form in the linear KF. In contrast I find the equations in Crassidis do not seem to converge much.\n", + "\n", + "I am not comfortable saying either book is wrong; it is quite likely that I missed some point that makes each set of equations work. I can say that when I implemented them as written I did not get a filter that worked. Between reading implementation notes on the web and reasoning about various issues I have chosen the implementation in this chapter, which does in fact seem to work correctly. I have yet to explore the significant amount of original literature that will likely definitively explain the discrepencies. I would like to leave this here in some form even if I do find an explanation that reconciles the various differences, as if I got confused by these books than probably others will as well.\n", + "\n", + "## References\n", + "- [1] Crassidis, John L., and John L. Junkins. *Optimal estimation of dynamic systems*. CRC press, 2011.\n", + "\n", + "- [2] Brown, Robert Grover, and Patrick Y.C. Hwang. *Introduction to Random Signals and Applied Kalman Filtering, With MATLAB\u00ae excercises and solutions.* Wiley, 2012." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb b/12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb similarity index 100% rename from 11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb rename to 12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb diff --git a/12_HInfinity_Filters/HInfinity_Filters.ipynb b/13_HInfinity_Filters/HInfinity_Filters.ipynb similarity index 100% rename from 12_HInfinity_Filters/HInfinity_Filters.ipynb rename to 13_HInfinity_Filters/HInfinity_Filters.ipynb diff --git a/README.md b/README.md index 3e9a3a0..b5c3f07 100644 --- a/README.md +++ b/README.md @@ -136,36 +136,41 @@ Kalman filter as covered only work for linear problems. Extended Kalman filters * [**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb) - Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires. -* [**Chapter 11: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb) +[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter_Kalman_Filters.ipynb) + +Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems. + + +* [**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb) EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem. -* [**Chapter 12: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_HInfinity_Filters/HInfinity_Filters.ipynb) +* [**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters/HInfinity_Filters.ipynb) H-inifinity filters are a form of filter that is very robust in the presence of non-Gaussian noise. They do not perform as well as Kalman filters, but are less likely to diverge. -* [**Chapter 13: Numerical Stability**](not implemented) - -Not written yet. - - * [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb) Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches. + + +* [**Chapter XX: Numerical Stability**](not implemented) + +Not written yet. + -* [**Chapter 15: Particle Filters**](not implemented) +* [**Chapter XX: Particle Filters**](not implemented) Not written yet -* [**Chapter 16: Multihypothesis Tracking**](not implemented) +* [**Chapter XX: Multihypothesis Tracking**](not implemented) Not written yet. diff --git a/exp/1dposvel.ipynb b/exp/1dposvel.ipynb new file mode 100644 index 0000000..aaae8ec --- /dev/null +++ b/exp/1dposvel.ipynb @@ -0,0 +1,415 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:11e1418ff66f54f922969f038a790dfe89ea35dd53a49764ef0cb06a9c32f67b" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "from __future__ import division, print_function\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from numpy.random import randn\n", + "from numpy import array, asarray\n", + "\n", + "import sys\n", + "sys.path.insert(0, '../code') # allow us to import book_format\n", + "import book_format\n", + "book_format.load_style()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "\n", + "\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pos = 1\n", + "vel = 1\n", + "p_pos = 1\n", + "p_move = .5\n", + "\n", + "data = [(0,pos+randn()*p_pos) for i in range(1000)]\n", + "data = np.asarray(data)\n", + "\n", + "pos += vel\n", + "\n", + "\n", + "plt.scatter(data[:,0], data[:,1], marker=',')\n", + "plt.show()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from filterpy.kalman import KalmanFilter\n", + "from numpy.random import multivariate_normal\n", + "import matplotlib.cm as cm\n", + "\n", + "vel = 20.\n", + "kf = KalmanFilter(dim_x=2, dim_z=1)\n", + "kf.F = array([[1, 1], [0, 1]], dtype=float)\n", + "kf.H = array([[1, 0]])\n", + "kf.x = array([[0., vel]]).T\n", + "kf.P *= 100\n", + "kf.R *= 10\n", + "kf.Q *= .0001\n", + "\n", + "steps = 6\n", + "colors = cm.winter(np.linspace(0, .5, steps))\n", + "pcolors = cm.Reds(np.linspace(.5, 1, steps))\n", + "for i in range(steps):\n", + " kf.predict()\n", + " x,y = multivariate_normal(mean=kf.x.T[0], cov=kf.P, size=1000).T\n", + " plt.scatter(x,y, marker=',', c=pcolors[i])\n", + " \n", + " z = (i+1)*vel\n", + " kf.update(z)\n", + " x,y = multivariate_normal(mean=kf.x.T[0], cov=kf.P, size=1000).T\n", + " plt.scatter(x,y, marker='.', c=colors[i], alpha=0.7)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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DUHjooRK0/e3cUOvWUscWrEAv14Hc/s4xfxn4ywglWnMCdLqfcuvCm90WdT3d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P1P5CBz5sxhzhynZyQajfLqq69+4H1+YOJsmiZXXHEF06dP51vf+tb4+unTp7Njxw4WLnSSY7Zv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+ "text": [ + "" + ] + } + ], + "prompt_number": 99 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def update(mu1, var1, mu2, var2):\n", + " if var1 == 0.0:\n", + " var1=1.e-80\n", + " \n", + " if var2 == 0:\n", + " var2 = 1e-80\n", + " \n", + " mean = (var1*mu2 + var2*mu1) / (var1+var2)\n", + " variance = 1. / (1/var1 + 1/var2)\n", + " return (mean, variance)\n", + "\n", + "\n", + "\n", + "def predict(mu1, var1, mu2, var2):\n", + " return (mu1+mu2, var1+var2)\n", + "\n", + "pos = 0\n", + "pos_p = 100.\n", + "move = 1\n", + "move_p = .1\n", + "sense_p = 8\n", + "\n", + "N = 10\n", + "colors = cm.winter(np.linspace(0, .5, N))\n", + "pcolors = cm.Reds(np.linspace(.5, 1, N))\n", + "for i in range(N):\n", + " pos, pos_p = predict(pos, pos_p, move, move_p)\n", + " x,y = multivariate_normal(mean=(pos,i), cov=[[0.001, 0],[0, p_pos]], size=1000).T\n", + " plt.scatter(x,y, marker=',', c=colors[i]) \n", + " print(pos, pos_p)\n", + " \n", + " pos, pos_p = update(pos, pos_p, i, sense_p)\n", + " x,y = multivariate_normal(mean=(i, pos), cov=asarray([[0.001, 0],[0, pos_p]]).T, size=1000).T\n", + " plt.scatter(x,y, marker='.', c=pcolors[i], alpha=0.5) \n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1 100.1\n", + "1.0740055504162813 7.50795559666975\n", + "2.0381768183201006 3.9730859395971154\n", + "3.02550842348427 2.7546779733418045\n", + "4.018974755764885 2.1491012228687625\n", + "5.014956796940505" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 1.7940228898505703\n", + "6.012217081465885 1.5654022438192952\n", + "7.010217725218009 1.409220211690132\n", + "8.008687415099768 1.2981611057964606\n", + "9.007474523188765 1.2169185743509554\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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9985sYT1PVpOH0ipvnJ35OD0AKBqu6zGoN/O0ditVP7jHOVgJ88rK30Tv6iJU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\ney/79++no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+ "text": [ + "" + ] + } + ], + "prompt_number": 100 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb index d249c07..089facf 100644 --- a/table_of_contents.ipynb +++ b/table_of_contents.ipynb @@ -75,41 +75,40 @@ "\n", "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n", "\n", - "*Still early going.*\n", + "[**Chapter 11: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filter_Kalman_Filters.ipynb)\n", "\n", + "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n", "\n", - "\n", - "[**Chapter 11: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)\n", + "[**Chapter 12: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)\n", "\n", "Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n", "\n", "\n", - "[**Chapter 12: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12_HInfinity_Filters/HInfinity_Filters.ipynb)\n", + "[**Chapter 13: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_HInfinity_Filters/HInfinity_Filters.ipynb)\n", " \n", "Describes the $H_\\infty$ filter. \n", "\n", "*I have code that implements the filter, but no supporting text yet.*\n", "\n", "\n", - "[**Chapter 13: Numerical Stability**](not implemented)\n", + "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb)\n", + " \n", + "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n", + " \n", + "[**Chapter XX: Numerical Stability**](not implemented)\n", "\n", "EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n", "\n", "*This chapter is not started. I'm likely to rearrange where this material goes - this is just a placeholder.*\n", "\n", - "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb)\n", - " \n", - "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n", - " \n", - " \n", - "[**Chapter 15: Particle Filters**](not implemented)\n", + "[**Chapter XX: Particle Filters**](not implemented)\n", " \n", "Particle filters uses a Monte Carlo technique to filter. \n", "\n", "*This is not implemented, and I have not decided if I want to make it part of this book or not.*\n", " \n", "\n", - "[**Chapter 16: Multihypothesis Tracking**](not implemented)\n", + "[**Chapter XX: Multihypothesis Tracking**](not implemented)\n", " \n", "*Not implemented yet.*\n", "\n",