daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Work on animations.

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Experimented with adding animations to the book. Also, fixed
the H-Infinity code a bit to more close reflect Simon's code.
This commit is contained in:
Roger Labbe committed 2014-09-08 22:04:42 -07:00
1 parent d8507caf9b
commit 9a8616a508
3 files changed
+105 -104
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Chapter12_HInfinity_Filters/HInfinity_Filters.ipynb
+16 -58
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:7d487f11e312a7f42c8b0f26c54306db69a8a6b3fcec7863bf7db4070d9fc17e"
"signature": "sha256:8c68f422d155e6e256b984c1601ed17d878240844f686e8559510bac3b24b183"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -256,13 +256,13 @@
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"prompt_number": 2,
"text": [
"<IPython.core.display.HTML at 0x7f16501445f8>"
"<IPython.core.display.HTML at 0x7f5f82564358>"
]
}
],
"prompt_number": 4
"prompt_number": 2
},
{
"cell_type": "markdown",
@@ -291,96 +291,54 @@
"from filterpy.hinfinity import HInfinityFilter\n",
"\n",
"dt = 0.1\n",
"f = HInfinityFilter(2, 1, 0, gamma=.4)\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",
"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\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",
" #print(f.x.T)\n",
" xs.append(f.x[0,0])\n",
" vs.append(f.x[1,0])\n",
" f.predict()\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.plot(vs) \n",
"plt.title('velocity')\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"[[ 0.66806083 3.34442646]]\n",
"[[ 1.1284151 3.77161316]]\n",
"[[ 1.60570517 4.03126553]]\n",
"[[ 2.09144161 4.20685183]]\n",
"[[ 2.58197401 4.33419113]]\n",
"[[ 3.07547127 4.4312003 ]]\n",
"[[ 3.57091268 4.50783208]]\n",
"[[ 4.06768385 4.57005637]]\n",
"[[ 4.56539275 4.62167173]]\n",
"[[ 5.06377767 4.66521177]]\n",
"[[ 5.56265755 4.7024333 ]]\n",
"[[ 6.06190354 4.73459514]]\n",
"[[ 6.5614219 4.76262524]]\n",
"[[ 7.0611432 4.78722492]]\n",
"[[ 7.56101536 4.80893611]]\n",
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"[[ 8.56106372 4.8453174 ]]\n",
"[[ 9.06118672 4.86060987]]\n",
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"[[ 13.56300681 4.94417805]]\n",
"[[ 14.06319168 4.94951612]]\n",
"[[ 14.563366 4.95433916]]\n",
"[[ 15.0635295 4.95869805]]\n",
"[[ 15.56368218 4.96263833]]\n",
"[[ 16.06382423 4.96620086]]\n",
"[[ 16.56395596 4.96942231]]\n",
"[[ 17.06407777 4.97233571]]\n",
"[[ 17.56419015 4.97497078]]\n",
"[[ 18.0642936 4.97735429]]\n",
"[[ 18.56438866 4.97951041]]\n",
"[[ 19.06447587 4.98146094]]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f164f16d518>"
"<matplotlib.figure.Figure at 0x7f5f616b2ac8>"
]
}
],
"prompt_number": 5
"prompt_number": 14
}
],
"metadata": {}