Renamed Chapters

Renamed chapters to make them easier to work with from the command line using tab completion.
2014-09-28 10:40:06 -07:00
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+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "$H_\\infty$ filter"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#format the book\n",
+      "%matplotlib inline\n",
+      "from __future__ import division, print_function\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",
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+     "source": [
+      "I am still mulling over how to write this chapter. In the meantime, Dan Simon has an accessible introduction here:\n",
+      "\n",
+      "http://academic.csuohio.edu/simond/courses/eec641/hinfinity.pdf\n",
+      "\n",
+      "In one sentence the $H_\\infty$ (H infinity) filter is like a Kalman filter, but it is robust in the face of non-Gaussian, non-predictable inputs.\n",
+      "\n",
+      "\n",
+      "My filterpy library contains an H-Infinity filter. I've pasted some test code below which implements the filter designed by Simon in the article above. Hope it helps."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import (absolute_import, division, print_function,\n",
+      "                        unicode_literals)\n",
+      "\n",
+      "from numpy import array\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "from filterpy.hinfinity import HInfinityFilter\n",
+      "\n",
+      "dt = 0.1\n",
+      "f = HInfinityFilter(2, 1, dim_u=1, gamma=.01)\n",
+      "\n",
+      "f.F = array([[1., dt],\n",
+      "             [0., 1.]])\n",
+      "\n",
+      "f.H = array([[0., 1.]])\n",
+      "f.x = array([[0., 0.]]).T\n",
+      "f.G = array([[dt**2 / 2, dt]]).T\n",
+      "\n",
+      "f.P = 0.01\n",
+      "f.W = array([[0.0003, 0.005],\n",
+      "             [0.0050, 0.100]])/ 1000  #process noise\n",
+      "\n",
+      "f.V = 0.01\n",
+      "f.Q = 0.01\n",
+      "u = 1. #acceleration of 1 f/sec**2\n",
+      "\n",
+      "xs = []\n",
+      "vs = []\n",
+      "\n",
+      "for i in range(1,40):\n",
+      "    f.update (5)\n",
+      "    #print(f.x.T)\n",
+      "    xs.append(f.x[0,0])\n",
+      "    vs.append(f.x[1,0])\n",
+      "    f.predict(u=u)\n",
+      "\n",
+      "plt.subplot(211)\n",
+      "plt.plot(xs)\n",
+      "plt.title('position')\n",
+      "plt.subplot(212)\n",
+      "plt.plot(vs) \n",
+      "plt.title('velocity')\n",
+      "plt.show()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
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+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f5f616b2ac8>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}