Appendix-D-HInfinity-Filters.ipynb

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    "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
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   "source": [
    "# H Infinity filter"
   ]
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   "source": [
    "#format the book\n",
    "%matplotlib inline\n",
    "from __future__ import division, print_function\n",
    "from book_format import load_style\n",
    "load_style()"
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   "source": [
    "I am still mulling over how to write this chapter. In the meantime, Professor Dan Simon at Clevant State University 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."
   ]
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8g6KiIjzxxBPYt28fTp8+jVtvvRWzZs3C//3f/+H48eN+J+UpFAq43W5x+dZb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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bd271699e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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.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');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}