Text used sigma_ps when talking about the wheel.

08-Designing-Kalman-Filters.ipynb

@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -341,9 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -373,15 +369,14 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEPCAYAAABvDuGDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a15dc5f28>"
+       "<matplotlib.figure.Figure at 0x162c6b6d320>"
       ]
      },
      "metadata": {},
@@ -461,9 +456,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from filterpy.kalman import KalmanFilter\n",
@@ -489,7 +482,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -536,9 +528,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -593,9 +583,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tracker.H = np.array([[1/0.3048, 0, 0,        0],\n",
@@ -619,9 +607,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -663,9 +649,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tracker.x = np.array([[0, 0, 0, 0]]).T\n",
@@ -690,15 +674,14 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAESCAYAAACxXtKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16ec7b00>"
+       "<matplotlib.figure.Figure at 0x162c792e940>"
       ]
      },
      "metadata": {},
@@ -774,9 +757,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -800,9 +781,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -816,7 +795,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16f8b128>"
+       "<matplotlib.figure.Figure at 0x162c79dfe80>"
       ]
      },
      "metadata": {},
@@ -841,9 +820,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -919,7 +896,6 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -927,7 +903,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16fbfa20>"
+       "<matplotlib.figure.Figure at 0x162c79a8470>"
       ]
      },
      "metadata": {},
@@ -1004,9 +980,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def ZeroOrderKF(R, Q, P=20):\n",
@@ -1068,9 +1042,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def FirstOrderKF(R, Q, dt):\n",
@@ -1134,9 +1106,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def SecondOrderKF(R_std, Q, dt, P=100):\n",
@@ -1178,9 +1148,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def simulate_system(Q, count):\n",
@@ -1204,9 +1172,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def filter_data(kf, zs):\n",
@@ -1231,15 +1197,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17031908>"
+       "<matplotlib.figure.Figure at 0x162c7b13f60>"
       ]
      },
      "metadata": {},
@@ -1267,9 +1231,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_residuals(xs, filter_xs, Ps, title, y_label, stds=1):\n",
@@ -1283,15 +1245,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a170a8128>"
+       "<matplotlib.figure.Figure at 0x162c8b845f8>"
       ]
      },
      "metadata": {},
@@ -1316,15 +1276,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a181cf358>"
+       "<matplotlib.figure.Figure at 0x162c8b845c0>"
       ]
      },
      "metadata": {},
@@ -1349,15 +1307,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvkAAAEmCAYAAAD4PI8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1815c208>"
+       "<matplotlib.figure.Figure at 0x162c8bc1550>"
       ]
      },
      "metadata": {},
@@ -1387,15 +1343,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1827fb70>"
+       "<matplotlib.figure.Figure at 0x162c7b4f588>"
       ]
      },
      "metadata": {},
@@ -1423,15 +1377,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvkAAAEmCAYAAAD4PI8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFUcXwOHfXpBeBKSo2LBrVLAnNkSN3WDs2DGW2ONn\nxI69xxob9q7RGI2x99gjUdGoMUaxS1FBRel3vz9uvISgAgqCcN7n4QmzOzt71g3cw+zsjKKqqooQ\nQgghhBAiy9BkdABCCCGEEEKItCVJvhBCCCGEEFmMJPlCCCGEEEJkMZLkCyGEEEIIkcVIki+EEEII\nIUQWY5jRAaSlp0+fZnQIQgghhPjArK2tMzoEITId6ckXQgghhBAii5EkXwghhBBCiCwmSw3X+be3\nPbrz9/cHoGLFih8qHPEWcj8yF7kfmYvcj8xF7kfmIUN0hXg76ckXQgghhBAii5EkXwghhBBCiCxG\nknwhhBBCCCGyGEnyhRBCCCGEyGIkyRdCCCGEECKLkSRfCCGEEEKILEaSfCGEECKbOHv2LPXq1aNI\nkSLUr1+f8+fPZ3RIQoh0kmXnyRdCCCFEgoCAAGrVqkVkZCQAN27c4MSJE/z222+UKlUqg6MTQqQ1\n6ckXQgghsoHvvvuOyMhImjdvTkBAAE2aNOHFixfMnj07o0MTQqQDSfKFEEKIbCA4OBiADh06ULZs\nWdq1awdAUFBQRoYlhEgnMlxHCCGEyAbKlCnDvn378PHx4c8//2Tp0qUAlC1bNoMjE0KkB+nJF0II\nIbKBkSNHUrp0af7++29GjBhBYGAgrq6u+Pj4ZHRoQoh0ID35QgghRDaQM2dOTp8+zbJlywgMDKRw\n4cJ069YNMzOzjA5NCJEOJMkXQgghsgkLCwsGDBiQ0WEIIT4AGa4jhBBCiExh79693LhxI6PDECJL\nkCRfCCGEEBlCVVWePXumLx86dIglS5ZkYERCZB2S5AshhBDZjFarZnQIACxbtozevXvry97e3pQo\nUSIDIxIi68jQJH/y5MlUrlwZa2trHBwcaNasGZcvX05Sb8yYMeTNmxczMzNq167NlStXMiBaIYQQ\n4uPXb6aKsTvkbabS4BuVb79XWbNH5cJfKtEx6Zv8BwYG0q9fP325WbNmBAQEoNVqAShevDhdunQh\nLi4uXeMQIjvI0CT/119/pW/fvpw6dYrDhw9jaGhI3bp1CQ8P19eZOnUqs2bNYv78+fj7++Pg4EC9\nevV48eJFBkYuhBBCfHz2nlGZ/yPEx8PDx7DvN/huA3QeD+W7gkVdKNNBxctXZfJqlZ0nVR6Fv3vi\nr9VqOXToEKqqayNPnjxs2LCBO3fuAODg4MDFixfRaHTpyNq1a8mbNy85cuSgcOHC7Nmz5/0vWohs\nKkNn19m9e3ei8po1a7C2tubEiRM0btwYgDlz5jBs2DA8PT0BWLVqFQ4ODqxfv57u3bt/8JiFEEKI\nj1FcnMrgeW+vEx8PlwN1XxsPJGwvVVClejmo8c9XficlRedUFIVevXqxevVqqlatirGxMXv27MHe\n3j5RHYBffvmFjh076rffvHmTpk2bcurUKSpWrJjyCxVCAJlsTP6zZ8/QarXY2NgAusd6QUFB1KtX\nT1/HxMSEmjVrcvLkyYwKUwghhPjoLN2hS94BzE3h5GLYPAFGdYXmNaFw3jcfe+UW+G2HjuOgYAso\n1EKl0zgVv+0qf95W9T31AAMHDuSnn34CdAm8j48PYWFh+v0VK1bE1NQ0yTkWLVoEgI+PDzExMXTs\n2JG4uDiWLVv2/hcvRDaUqebJHzBgAOXLl+fTTz8FICgoCEVRcHR0TFTP0dGRBw8evLUtf3//ZM+X\nkjriw5H7kbnI/chc5H5kLh/b/YiI1DBi0SdADgA6etzHMCqIApZQwBWauurqvYzWcOOhKTcemHL9\ngSlX75px9Y458drEPfe3g3Rfa/eC+vIKVgb36PBlaTrWCcLa2pp58+aRL18+AMqVKwck/2/26nPd\nzs6OgIAA/Wd/YGDga48tWrToO/97CJEdZJokf9CgQZw8eZITJ07oH90JIYQQWdHTp0/ZuHEj9+7d\nI0+ePLRr146cOXOm2/mW78tNWIQuwXeyicbLPfi19cyMtZQp+AIr5Qrn9izD4OFD6uZxoXqDQdx9\nWpDzNyy4dMucyGgFRflnMED0XZ7dGcnCnWc5dMGMsR2aUKdOnVTHWK5cOc6fP8+kSZM4evQohw8f\n1m8XQqSeov77GVsG+eabb/jhhx84cuRIor/MXy27ffbsWSpUqKDf3qRJE+zt7VmxYkWidp4+far/\n3tra+o3ne9UjIGP8Mge5H5mL3I/MRe5H5pIW9+Px48dUqVIl0aJPBQoU4MyZM0meXKeFm/dVSrWH\nmFhded0YaFfvzZ1p169fp3LlyokmwbC3t8ff35/8+fNz+859POrUp2Xfg8xa9juxxhUheDHk9UHR\n5MDMWMucbzR4NyFVnXaRkZE0btxYn9wDtGnThnXr1mFgYJCkfko/84XIrjJ8TP6AAQPYtGkThw8f\nTvLorVChQjg5ObF//379tqioKI4dO0a1atU+dKhCCCHEe5syZQo3btygdOnSLF68mHLlynH79m0m\nTJiQLucbujAhwa9aGtrWfXv9yZMnEx4eTp06ddi7dy/VqlUjNDSUyZMnA1Agf17s7Sy4uKcLsZca\n427hRacOLdD8k9C/jNbQfQq0GA6hYSnvRzQ1NWXrtn0MmnyOed8v4sCBA2zYsOG1Cb4QInkZOlyn\nT58+rF27lu3bt2NtbU1wsO7xoYWFBebm5oDuBZ7JkydTvHhxihYtyoQJE7C0tKRdu3YZGboQQgjx\nTq5duwbAyJEjadu2Lfb29nz55Zf67Wnp2AWVLQkd48zsn3zv+qvpLfv06cPnn39OaGgoJ06c4PTp\n0/o6Bw8epEmTJgAMHvwNjRuXovDMnfiuKYRiVhKAbb/CqT9g2TCVRp+9+ZxarcqvF2DVLthyxIAX\nka5sn+pKneoydFeI95GhSf7ChQtRFCXJ2D1fX19Gjx4NwJAhQ4iKiqJv376EhYVRpUoV9u3bp/8j\nQAghhPiYvHohddGiRTg4OLBgwQIA8ufPn6bn0WpVBs1NKLerB1U/ST5xLlasGAcPHqRPnz6Eh4cz\nc+ZMAAoWLKivY25uTpEiRThy5AgTJ07k6dOnbF09DS79hVvz/Zx/+BkAwU+gybfw9Zcq0/uAmUnC\n+W/eV1m9B1bvhlsPE8ewahc0rf7u1y6EyCRj8tOKjMn/OMn9yFzkfmQucj8yl7S4H4GBgVSsWJEn\nT57ot1lbW3P27Nk0nTFm9W6VLv+MADIxgj83vHl++8uXL3P48GH69u3Lw4cPcXV1JSQkRL/fxcWF\n3377DTs7O/22u3fvUqlSJf1TeND9IXD27FnOXrej22QIepxwjuL5YbEP3LwPq3bD0fOvj7tUQfiq\nGQxs8/Y/SGRMvhBvl2lm1xFCCCGyg0KFCvHbb78xevRort+4jUvBfIwdOyZNE/wXkSrDFyWUB7VL\nnODHx8dz4cIF/aQWVlZW+Pr68tVXX5E7d24CAgKYMWMGd+/epUiRIgwaNChRgg+6JxL+/v7MmDGD\n27dvU6RIEYYMGUKuXLlomAsurlbpORV++lVX/9odcO/z+nhtLHVPGro0ggolUvfCrhDi9STJF0II\nIT4wFxcXrN3W4n8L7r6AoSugSmmVqqWhYgmwMHu/JHf6enjwSPe9kx34tE+8Pyoqijp16nDt2jUc\nHR3Jly8fS5cu1S9q5eTkxIwZM5I9j7OzM7Nnz37tvlw5FbZMUlm5CwbMUomITHxNBgbQoAp0bgRN\nq4GxkST2QqSlDJ9dRwghhMhu1u2DRbpFYQl+AtuPwfBF4NEPctYHt84qvaaprNypW1FWq035yNr7\noSrT1yWUx3cHS3OFjh07cuXKFUA3pr5Pnz7cunVLX6958+avXYn2fSiKQmv3lxSNbIP6VNelr778\ng+Imy7jxQxw7piu0rK1Igi9EOpAkXwghhPiA7oWo9Jv55v1aLQT8DX7bwXsSlPKCXI2gwxiVnSdV\nYuPenvCPWASR0aBG3aSEw190aaTbnj9/fpYuXaqvN3HiRKpUqZIWl/RW48eP59zJLTg9aYf3J99i\nc8edPw91Z8v6eel+biGyMxmuI4QQQnwgqqry1WR4GqEru+SBHybAhetw+jKcuQx/3IT/TokR/hzW\n79d92VpBy9oqXp9D9bKg0ST0gvtf1c1YA0DYTpxyncLAYAMA//vf/9BoPnzf3pkzZwBYuGABnp6e\nVCpflF69evHbb7998FiEyE4kyRdCCCE+kMXbYN8/ua2iwIoRUL64Qvni4K2bdp5nL1T8/4TTf+iS\n/tOXITRh8VmePNP18vttB2cHaFNHpV09MIi6SMMWk8BxIwCNmnWgYOw1VFVFURRsbW0/8NXq5MyZ\nE4D9+/dTs2ZN/Yq2r7YLIdKHJPlCCCHEB3Djnsq38xPKg9pCDdekY9GtzBU8KoCHbuIbVFUl4Dps\nOAAbD8Dd4Ffbtdz96yAzguvy3QYFZ/sSPLpzFKxvYmThwuz/2VA03/wk7X9o/fr1Y/v27SxYsEC/\nJoCRkRG9evXK4MiEyNpkTL4QQgiRzuLjVbpOhBeRunKpgroXYlPizp07hD84Su/GdwjcAkfnQ6/m\nYGetQGB/eH4cgHuhRlDWH4wL0acFFM2XOV5m9fDw4KeffqJ48eKYmppSpkwZ9uzZg6ura0aHJkSW\nJkm+EEIIkc5m/wDHL+q+NzCAVaPAxPjtSbiqqgwZMoSCBQtSu3ZtChYsSI0a1Xl8azsLBis83KHw\nv29HULvcM8z/mRRHMc6LjWUcIzun8wWl0hdffMGff/7Jy5cvuXjxIrVr187okITI8Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182de9e8>"
+       "<matplotlib.figure.Figure at 0x162c8cddeb8>"
       ]
      },
      "metadata": {},
@@ -1460,7 +1412,6 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1468,7 +1419,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1826c470>"
+       "<matplotlib.figure.Figure at 0x162c8cc40b8>"
       ]
      },
      "metadata": {},
@@ -1500,15 +1451,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAogAAAE6CAYAAACVo6NZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1711fe48>"
+       "<matplotlib.figure.Figure at 0x162c79130f0>"
       ]
      },
      "metadata": {},
@@ -1544,15 +1493,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182de630>"
+       "<matplotlib.figure.Figure at 0x162c8cdd2e8>"
       ]
      },
      "metadata": {},
@@ -1578,15 +1525,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a170f82b0>"
+       "<matplotlib.figure.Figure at 0x162c7906390>"
       ]
      },
      "metadata": {},
@@ -1596,7 +1541,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a18222048>"
+       "<matplotlib.figure.Figure at 0x162c8d41b00>"
       ]
      },
      "metadata": {},
@@ -1631,15 +1576,13 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16ed1ac8>"
+       "<matplotlib.figure.Figure at 0x162c79ccb70>"
       ]
      },
      "metadata": {},
@@ -1649,7 +1592,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1712fc50>"
+       "<matplotlib.figure.Figure at 0x162c8e7a400>"
       ]
      },
      "metadata": {},
@@ -1684,15 +1627,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAEWCAYAAADmVqp5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182942e8>"
+       "<matplotlib.figure.Figure at 0x162c8d77ef0>"
       ]
      },
      "metadata": {},
@@ -1720,15 +1661,13 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16f359b0>"
+       "<matplotlib.figure.Figure at 0x162c7989240>"
       ]
      },
      "metadata": {},
@@ -1776,15 +1715,13 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a18350390>"
+       "<matplotlib.figure.Figure at 0x162c8d5f0f0>"
       ]
      },
      "metadata": {},
@@ -1794,7 +1731,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16fbf048>"
+       "<matplotlib.figure.Figure at 0x162c8c2aa90>"
       ]
      },
      "metadata": {},
@@ -1827,15 +1764,13 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17102518>"
+       "<matplotlib.figure.Figure at 0x162c8d62518>"
       ]
      },
      "metadata": {},
@@ -1845,7 +1780,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182f7c50>"
+       "<matplotlib.figure.Figure at 0x162c79a1c50>"
       ]
      },
      "metadata": {},
@@ -1873,15 +1808,13 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16f4c518>"
+       "<matplotlib.figure.Figure at 0x162c8b88b38>"
       ]
      },
      "metadata": {},
@@ -1891,7 +1824,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17126ac8>"
+       "<matplotlib.figure.Figure at 0x162c8bcef28>"
       ]
      },
      "metadata": {},
@@ -1972,9 +1905,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2053,15 +1984,13 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a170a2b38>"
+       "<matplotlib.figure.Figure at 0x162c8e27d30>"
       ]
      },
      "metadata": {},
@@ -2094,15 +2023,13 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAApAAAAEWCAYAAADRtKcIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16ee2e80>"
+       "<matplotlib.figure.Figure at 0x162c8ba6a58>"
       ]
      },
      "metadata": {},
@@ -2171,15 +2098,21 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 10.0  0.0]\n",
+      " [ 0.0  10.0]]\n"
+     ]
+    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAEmCAYAAAAA8LatAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1709c358>"
+       "<matplotlib.figure.Figure at 0x162c8cd2240>"
       ]
      },
      "metadata": {},
@@ -2193,9 +2126,10 @@
     "kf.P *= 10\n",
     "kf.R *= R\n",
     "kf.Q = Q_discrete_white_noise(2, dt, 0.1)\n",
-    "kf.F = np.array([[1., 0], [0., 1]])\n",
-    "kf.B = np.array([[dt], [ 1]])\n",
+    "kf.F = np.array([[1., 0], [0., 0.]])\n",
+    "kf.B = np.array([[dt], [ 1.]])\n",
     "kf.H = np.array([[1., 0]])\n",
+    "print(kf.P)\n",
     "\n",
     "zs = [i + randn()*R for i in range(1, 100)]\n",
     "xs = []\n",
@@ -2262,9 +2196,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2277,7 +2209,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17078550>"
+       "<matplotlib.figure.Figure at 0x162c8baddd8>"
       ]
      },
      "metadata": {},
@@ -2352,7 +2284,6 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2360,7 +2291,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAEWCAYAAADB+CuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1709c470>"
+       "<matplotlib.figure.Figure at 0x162c8caaf28>"
       ]
      },
      "metadata": {},
@@ -2388,7 +2319,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2396,7 +2326,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182a1630>"
+       "<matplotlib.figure.Figure at 0x162c8beb240>"
       ]
      },
      "metadata": {},
@@ -2440,15 +2370,13 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAEWCAYAAADB+CuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182010b8>"
+       "<matplotlib.figure.Figure at 0x162c7ad47b8>"
       ]
      },
      "metadata": {},
@@ -2528,7 +2456,6 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2536,7 +2463,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a170c3208>"
+       "<matplotlib.figure.Figure at 0x162c8c50518>"
       ]
      },
      "metadata": {},
@@ -2546,7 +2473,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a18135748>"
+       "<matplotlib.figure.Figure at 0x162c8c4f668>"
       ]
      },
      "metadata": {},
@@ -2556,7 +2483,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16f54da0>"
+       "<matplotlib.figure.Figure at 0x162c7a7be10>"
       ]
      },
      "metadata": {},
@@ -2607,15 +2534,13 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182afa20>"
+       "<matplotlib.figure.Figure at 0x162c79614e0>"
       ]
      },
      "metadata": {},
@@ -2625,7 +2550,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182b4dd8>"
+       "<matplotlib.figure.Figure at 0x162c7961c18>"
       ]
      },
      "metadata": {},
@@ -2635,7 +2560,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a182d7e48>"
+       "<matplotlib.figure.Figure at 0x162c79e2f60>"
       ]
      },
      "metadata": {},
@@ -2686,9 +2611,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2729,7 +2652,6 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2744,7 +2666,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a18153630>"
+       "<matplotlib.figure.Figure at 0x162c7af9dd8>"
       ]
      },
      "metadata": {},
@@ -2819,9 +2741,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2880,22 +2800,20 @@
     "\n",
     "$$\\begin{aligned}\n",
     "\\mathbf H &= \\begin{bmatrix}1 &0\\end{bmatrix} \\\\\n",
-    "\\mathbf R &= \\sigma_{ps}^2\n",
+    "\\mathbf R &= \\sigma_{wheel}^2\n",
     "\\end{aligned}$$"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a16eeac88>"
+       "<matplotlib.figure.Figure at 0x162c6fcb9e8>"
       ]
      },
      "metadata": {},
@@ -3017,9 +2935,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import radians, sin, cos\n",
@@ -3087,15 +3003,13 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1815ecc0>"
+       "<matplotlib.figure.Figure at 0x162c8c7d9b0>"
       ]
      },
      "metadata": {},
@@ -3314,9 +3228,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sin,cos,radians\n",
@@ -3353,15 +3265,13 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAEWCAYAAADmVqp5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a1707ea90>"
+       "<matplotlib.figure.Figure at 0x162c8c4ce10>"
       ]
      },
      "metadata": {},
@@ -3477,15 +3387,13 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a18281d30>"
+       "<matplotlib.figure.Figure at 0x162c7a68d68>"
       ]
      },
      "metadata": {},
@@ -3555,9 +3463,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import radians, sin, cos, sqrt, exp\n",
@@ -3626,15 +3532,13 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a13efb6a0>"
+       "<matplotlib.figure.Figure at 0x162c8c2d588>"
       ]
      },
      "metadata": {},
@@ -3697,15 +3601,13 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17040f98>"
+       "<matplotlib.figure.Figure at 0x162c7b6f710>"
       ]
      },
      "metadata": {},
@@ -3715,7 +3617,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17139588>"
+       "<matplotlib.figure.Figure at 0x162c796e6a0>"
       ]
      },
      "metadata": {},
@@ -3771,15 +3673,13 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17089208>"
+       "<matplotlib.figure.Figure at 0x162c8e2f1d0>"
       ]
      },
      "metadata": {},
@@ -3789,7 +3689,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16a17108f98>"
+       "<matplotlib.figure.Figure at 0x162c79807f0>"
       ]
      },
      "metadata": {},
@@ -3839,9 +3739,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }