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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  },
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   "metadata": {},
   "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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cvhoGWGX4op6CoYyIiNpN0PBVfgHlllaGL0M6jPGZ9eErHYmxaVApGb6oZ2IoIyKikDmc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\npN0pogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b4f7a8e8d0>"
      ]
     },
     "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": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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
}