Fix of typos, added explanation for Q.

In response to reader's email.

10-Unscented-Kalman-Filter.ipynb

@@ -28,6 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,9 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
        "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-size: 115%;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -294,9 +295,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e2a400>"
+       "<matplotlib.figure.Figure at 0x713be48>"
       ]
      },
      "metadata": {},
@@ -357,7 +358,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x432c668>"
+       "<matplotlib.figure.Figure at 0x4f43ef0>"
       ]
      },
      "metadata": {},
@@ -403,14 +404,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=-0.014, y=43.369\n"
+      "Difference in mean x=-0.084, y=43.447\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApoAAAEWCAYAAADGljfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x866a7b8>"
+       "<matplotlib.figure.Figure at 0x7db60b8>"
       ]
      },
      "metadata": {},
@@ -463,9 +464,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x866ed30>"
+       "<matplotlib.figure.Figure at 0x7dc0898>"
       ]
      },
      "metadata": {},
@@ -503,7 +504,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a8a1d0>"
+       "<matplotlib.figure.Figure at 0x7d2c2e8>"
       ]
      },
      "metadata": {},
@@ -548,7 +549,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b8afd0>"
+       "<matplotlib.figure.Figure at 0x7f405c0>"
       ]
      },
      "metadata": {},
@@ -597,9 +598,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c58898>"
+       "<matplotlib.figure.Figure at 0x7fc2588>"
       ]
      },
      "metadata": {},
@@ -648,7 +649,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAApoAAAEWCAYAAADGljfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87249e8>"
+       "<matplotlib.figure.Figure at 0x8fa8eb8>"
       ]
      },
      "metadata": {},
@@ -862,9 +863,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86c39b0>"
+       "<matplotlib.figure.Figure at 0x93ed3c8>"
       ]
      },
      "metadata": {},
@@ -890,7 +891,7 @@
    "source": [
     "We can see that the sigma points lie between the first and second deviation, and that the larger $\\alpha$ spreads the points out. Furthermore, the larger $\\alpha$ weighs the mean (center point) higher than the smaller $\\alpha$, and weighs the rest of the sigma points less. This should fit our intuition - the further a point is from the mean the less we should weight it. We don't know *how* these weights and sigma points are selected yet, but the choices look reasonable.\n",
     "\n",
-    "Van der Merwe's formulation uses 3 parameters to control how the sigma points are distributed and weighted: $\\alpha$, $\\beta$, and $\\kappa$. We will go into the details later. For now, $\\beta=2$ is a good choice for Gaussian problems, $\\kappa=3-N$ is a good choice for $\\kappa$, and $0 \\le \\alpha \\le 1$ is an appropriate choice for $\\alpha$, where a larger value for $\\alpha$ spreads the sigma points further from the mean.\n",
+    "Van der Merwe's formulation uses 3 parameters to control how the sigma points are distributed and weighted: $\\alpha$, $\\beta$, and $\\kappa$. We will go into the details later. For now, $\\beta=2$ is a good choice for Gaussian problems, $\\kappa=3-n$ where $n$ is the dimension of $\\mathbf{x}$ is a good choice for $\\kappa$, and $0 \\le \\alpha \\le 1$ is an appropriate choice for $\\alpha$, where a larger value for $\\alpha$ spreads the sigma points further from the mean.\n",
     "\n",
     "### Sigma Point Computation\n",
     "\n",
@@ -898,7 +899,7 @@
     "\n",
     "$$ \\mathcal{X}_0 = \\mu$$\n",
     "\n",
-    "For notational convenience we define $\\lambda = \\alpha^2(n+\\kappa)-n$. Then the remaining sigma points are computed as\n",
+    "For notational convenience we define $\\lambda = \\alpha^2(n+\\kappa)-n$, where $n$ is the dimension of $\\mathbf{x}$. Then the remaining sigma points are computed as\n",
     "\n",
     "$$ \n",
     "\\boldsymbol{\\chi}_i = \\begin{cases}\n",
@@ -987,7 +988,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9721470>"
+       "<matplotlib.figure.Figure at 0x99c9a20>"
       ]
      },
      "metadata": {},
@@ -1000,13 +1001,13 @@
     "from numpy import random\n",
     "from numpy.random import randn\n",
     "\n",
-    "sigma_x, sigma_y = .3, .3\n",
+    "std_x, std_y = .3, .3\n",
     "dt = 1.0\n",
     "\n",
     "random.seed(1234)\n",
     "kf = KalmanFilter(4, 2)\n",
     "kf.x = np.array([0., 0., 0., 0.])\n",
-    "kf.R = np.diag([sigma_x**2, sigma_y**2])\n",
+    "kf.R = np.diag([std_x**2, std_y**2])\n",
     "kf.F = np.array([[1, dt, 0, 0], \n",
     "                 [0, 1, 0, 0],\n",
     "                 [0, 0, 1, dt],\n",
@@ -1017,8 +1018,8 @@
     "kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=1, var=0.02)\n",
     "kf.Q[2:4, 2:4] = Q_discrete_white_noise(2, dt=1, var=0.02)\n",
     "\n",
-    "zs = [np.array([i + randn()*sigma_x, \n",
-    "                i + randn()*sigma_y]) for i in range(100)]               \n",
+    "zs = [np.array([i + randn()*std_x, \n",
+    "                i + randn()*std_y]) for i in range(100)]               \n",
     "xs, _, _, _ = kf.batch_filter(zs)\n",
     "plt.plot(xs[:, 0], xs[:, 2]);"
    ]
@@ -1090,7 +1091,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The rest of the code is the same as for the linear kalman filter. Let's just implement it! I'll use the same measurements as used by the linear Kalman filter, and compute the standard deviation of the difference between the two solution."
+    "The rest of the code is the same as for the linear kalman filter. Let's just implement it! I'll use the same measurements as used by the linear Kalman filter, and compute the standard deviation of the difference between the two solutions."
    ]
   },
   {
@@ -1112,7 +1113,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x970b0f0>"
+       "<matplotlib.figure.Figure at 0x99c9748>"
       ]
      },
      "metadata": {},
@@ -1182,7 +1183,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x970dba8>"
+       "<matplotlib.figure.Figure at 0x9504860>"
       ]
      },
      "metadata": {},
@@ -1325,15 +1326,14 @@
     "    def __init__(self, pos, vel, vel_std):\n",
     "        self.pos = np.asarray(pos, dtype=float)\n",
     "        self.vel = np.asarray(vel, dtype=float)\n",
-    "        self.vel_std = vel_std\n",
-    "        \n",
+    "        self.vel_std = vel_std        \n",
     "        \n",
     "    def update(self, dt):\n",
     "        \"\"\" Compute and returns next position. Incorporates \n",
     "        random variation invelocity. \"\"\"\n",
     "        \n",
-    "        vel = self.vel*dt + (randn() * self.vel_std) * dt      \n",
-    "        self.pos += vel     \n",
+    "        dx = self.vel*dt + (randn() * self.vel_std) * dt      \n",
+    "        self.pos += dx     \n",
     "        return self.pos"
    ]
   },
@@ -1343,6 +1343,10 @@
    "source": [
     "Now let's put it all together. A military grade radar system can achieve 1 meter RMS range accuracy, and 1 mrad RMS for bearing [1]. We will assume a more modest 5 meter range accuracy, and 0.5$^\\circ$ angular accuracy as this provides a more challenging data set for the filter.\n",
     "\n",
+    "The design of $\\mathbf{Q}$ requires some discussion. The state $\\mathbf{x}$ contains $\\begin{bmatrix}x & \\dot{x} & y\\end{bmatrix}^\\mathtt{T}$. The first two elements are position (down range distance) and velocity, so we can use `Q_discrete_white_noise` noise to compute the values for the upper left hand side of Q. The third element of  $\\mathbf{x}$ is altitude, which we are assuming is independent of the down range distance. That leads us to a block design of $\\mathbf{Q}$ of:\n",
+    "\n",
+    "$$\\mathbf{Q} = \\begin{bmatrix}\\mathbf{Q}_\\mathtt{x} & 0 \\\\ 0 & Q_\\mathtt{y}\\end{bmatrix}$$\n",
+    "\n",
     "I'll start with the aircraft positioned directly over the radar station flying at 100 m/s. A typical radar might update only once every 12 seconds and so we will use that for our update period. "
    ]
   },
@@ -1350,15 +1354,14 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false,
-    "scrolled": false
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8724940>"
+       "<matplotlib.figure.Figure at 0x8fa8ef0>"
       ]
      },
      "metadata": {},
@@ -1368,7 +1371,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dfb0f0>"
+       "<matplotlib.figure.Figure at 0x7ff8908>"
       ]
      },
      "metadata": {},
@@ -1378,7 +1381,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c03080>"
+       "<matplotlib.figure.Figure at 0x7dd3240>"
       ]
      },
      "metadata": {},
@@ -1386,7 +1389,6 @@
     }
    ],
    "source": [
-    "import book_plots\n",
     "from filterpy.common import Q_discrete_white_noise\n",
     "import math\n",
     "from ukf_internal import plot_radar\n",
@@ -1403,7 +1405,7 @@
     "kf = UKF(3, 2, dt, fx=f_radar, hx=h_radar, points=points)\n",
     "\n",
     "kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
-    "kf.Q[2, 2] = 0.1\n",
+    "kf.Q[2,2] = 0.1\n",
     "\n",
     "kf.R = np.diag([range_std**2, bearing_std**2])\n",
     "kf.x = np.array([0., 90., 1100.])\n",
@@ -1458,7 +1460,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7aa2780>"
+       "<matplotlib.figure.Figure at 0x7df6ef0>"
       ]
      },
      "metadata": {},
@@ -1558,7 +1560,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7aa2080>"
+       "<matplotlib.figure.Figure at 0x7df6828>"
       ]
      },
      "metadata": {},
@@ -1629,7 +1631,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c384a8>"
+       "<matplotlib.figure.Figure at 0x8fad780>"
       ]
      },
      "metadata": {},
@@ -1678,7 +1680,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bbfa20>"
+       "<matplotlib.figure.Figure at 0x7df3cc0>"
       ]
      },
      "metadata": {},
@@ -1772,7 +1774,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c937b8>"
+       "<matplotlib.figure.Figure at 0x94a4240>"
       ]
      },
      "metadata": {},
@@ -1814,9 +1816,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c93c18>"
+       "<matplotlib.figure.Figure at 0x7d8ef28>"
       ]
      },
      "metadata": {},
@@ -1840,8 +1842,8 @@
     "    return [angle_a, angle_b]\n",
     "\n",
     "def fx_VOR(x, dt):\n",
-    "    x[0] += x[1]\n",
-    "    x[2] += x[3]\n",
+    "    x[0] += x[1] * dt\n",
+    "    x[2] += x[3] * dt\n",
     "    return x\n",
     "\n",
     "def hx_VOR(x):\n",
@@ -1900,7 +1902,7 @@
    "source": [
     "This looks quite good to me. There is a very large error at the beginning of the track, but the filter is able to settle down and start producing good data.\n",
     "\n",
-    "Let's revisit the nonlinearity of the angles. I will position the target between the two sensors with the same y-coordinate. This will cause a nonlinearity in the computation of the sigma means and the residuals because the mean angle will be near zero. As the angle goes below 0 the measurement function will compute a large positive angle of around $2\\pi$. The residual between the prediction and measurement will thus be very large, nearly $2\\pi$ instead of nearly 0. This makes it impossible for the filter to perform accurately, as seen in the example below."
+    "Let's revisit the nonlinearity of the angles. I will position the target between the two sensors with the same y-coordinate. This will cause a nonlinearity in the computation of the sigma means and the residuals because the mean angle will be near zero. As the angle goes below 0 the measurement function will compute a large positive angle of nearly $2\\pi$. The residual between the prediction and measurement will thus be very large, nearly $2\\pi$ instead of nearly 0. This makes it impossible for the filter to perform accurately, as seen in the example below."
    ]
   },
   {
@@ -1913,9 +1915,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c58eb8>"
+       "<matplotlib.figure.Figure at 0x7ca8e48>"
       ]
      },
      "metadata": {},
@@ -1961,9 +1963,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4VfW97/H3WnveGUGIMROTDIZZQAZrrZ5ThXMutsrR\n06gVpYgeEYKIEsoVKKCEShVULM56HFK9eKu1derp1dZTiYrWAyoICjJJsCgk7Oy1x7XuH6tsjKCA\nEpLA5/U8PM/O3nuFtZ4Vkk++fH/fn+E4joOIiIiIiBxxZkufgIiIiIjIsUphW0RERESkmShsi4iI\niIg0E4VtEREREZFmorAtIiIiItJMFLZFRERERJqJwraIiIiISDM5aNieM2cOpmk2+VNUVNTkPevW\nreOCCy6gXbt2ZGVlMWjQINauXdtsJy0iIiIi0hZ4D+VNvXr14tVXX8187PF4Mo83btzI6aefzuWX\nX86sWbPIz89n7dq1ZGdnH/GTFRERERFpSw4pbHs8HgoKCg742syZMxk5ciS33npr5rnOnTsfkZMT\nEREREWnLDqlne8OGDRQXF9O1a1cqKirYuHEjALZt8/vf/55TTjmFkSNHUlBQwGmnncZTTz3VrCct\nIiIiItIWGI7jON/0hhdffJFIJEKvXr3YsWMH8+fPZ+3atbz//vskEgmKiooIh8PMnz+fs88+mz/9\n6U/ceOONPPvss/zLv/zL0boOEREREZFW56Bh+6ui0ShdunShqqqKn/zkJxQXF3PxxRfz2GOPZd5z\nySWXsGvXLp5//vnMc/X19UfurEVEREREWkBeXt5hvf+wR/+Fw2F69+7NRx99RIcOHfB6vZSXlzd5\nT69evdi8efPhfmoRERERkWPKYYftWCzGmjVrOOmkk/D5fAwZMmS/MX/r1q3TIkkREREROe4ddBrJ\ntGnTOO+88ygtLeWzzz5j3rx5WJbF2LFjAbjxxhu56KKLOOOMMzjrrLN45ZVXePLJJ3n22We/9nMe\nbvldjqyVK1cCMHjw4BY+k+Ob7kProXvROug+tB66F62D7kPr8V3aoQ8atrdt20ZFRQU7d+6kY8eO\nDB8+nNraWkpLSwH40Y9+xL333sstt9xCZWUlPXr04NFHH2XUqFHf+qRERERERI4FBw3bNTU1B/0k\nY8eOzVS6RURERETEddg92yIiIiIicmgUtkVEREREmonCtoiIiIhIM1HYFhERERFpJgrbIiIiIiLN\nRGFbRERERKSZKGyLiIiIiDQThW0RERERkWaisC0iIiIi0kwUtkVEREREmonCtoiIiIhIM1HYFhER\nERFpJgrbIiIiIiLNRGFbRERERKSZKGyLiIiIiDQThW0RERERkWaisC0iIiIi0kwUtkVEREREmonC\ntoiIiIhIM1HYFhERERFpJgrbIiIiIiLNxNvSJyCHxrZttm5NsHu3TW6uSVmZH9PU70oiIiIirZnS\nWhtg2zb/7//F+O1vbc49N8jw4X5eeimGbdstfWoiIiIi8g0UttuALVuSPP+8SXV1kLo6k7o6k3Hj\ngmzZkmzpUxMRERGRb6CwLSIiIiLSTNSz3QaUlvr4l3+J0alTjOrqIAAPPhijtDTYwmcmIiIiIt9E\nYbsNME2Ts88O0qNHgrPOiv1jgWRQCyRFREREWjmF7TbCNN2AXVbW0mciIiIiIodKYVsASKVSfPBB\nAoDycj9er740RERERL6rg/YhzJkzB9M0m/wpKio64HuvuuoqTNPkV7/61RE/UWk+qVSKP/whxsMP\nmzz5pMErr1hs3GhptKCIiIjId3RITb+9evWirq4u82f16tX7vWf58uW89dZbFBUVYRjGET9RaT5r\n18b55BOTF17wUlDgcNllWYwYEeAPf7DYvFnzvEVERES+rUPqFfB4PBQUFHzt65s2bWLKlCn86U9/\nYuTIkUfs5OTosCyH6uoQo0cnM7O8ASZMCFFRkeCHP4zRp4/Brl2Odq8UEREROQyHlJg2bNhAcXEx\nXbt2paKigo0bN2ZeS6VSVFRUcNNNN9GzZ89mO1FpPh06eL72tUjEYOpUP3/6k7t75ahRXl59Ncon\nn8RIpVJs2hRn06a4qt8iIiIiB3DQsD1s2DAeeeQRXnrpJe677z7q6uoYMWIEX3zxBQCzZ8+moKCA\nq666qtlPVppHp04BHnwwxmuveaiqilFYaFNYaDNtWpynn/YxalSKGTNCJBIwfnySSy4JM3y4n2ee\niTNypIdhw3y8/LICt4iIiMhXGY7jOIdzQDQapUuXLlRVVTFw4EAuvfRS3n33XTp06ABAly5duPba\na7n++uubHFdfX595vH79+iNw6nIkmaZJItEej8fAcWD37jwqK7PZvdvkjjuiTJ4cZvToJM8958u0\nmRQW2oweneS++wIUFtr89reb8fu/IJFoD4Df/4UCuIiIiLR53bt3zzzOy8s7rGMPe75bOBymd+/e\nrF+/noaGBrZv385JJ52UeT2dTjN9+nSWLFnC5s2bD/fTSwuxbRuvdycAhgEdOnzOQw+1xzAMdu3K\noarKZNOmb/6PEMMw2LSpM9dckw/AffflcuKJjSQSWXi9KQKBT0mlUs19KSIiIiKtxmGH7Vgsxpo1\nazj77LO56qqruPDCCzOvOY7Dueeey8UXX8yVV175tZ9j8ODB3+5s5YhYuXIlcOj3wbZtyssTNDSk\nOessiwkTQgDceafFTTe5Ve2HHopz0kkn8uMf+6mrM2nf3mbz5gBXXukG75kzY5xySlcKCjzk5Xko\nKdEiy8O9D9J8dC9aB92H1kP3onXQfWg9vtyhcbgOGranTZvGeeedR2lpKZ999hnz5s3DsizGjh1L\nx44d6dixY5P3+3w+CgsLm5TbpW3bu3slQHm5TW1tEoDi4gBDhqSBNKWlAbZsSWaOGTMmybx5oUzL\nyc03B6moSFBT46eqKkbv3jF+8AM/27alASgt9R334VtERESOPQcN29u2baOiooKdO3fSsWNHhg8f\nTm1tLaWlpUfj/KSVMU2TTp0CmY87ddr3JVRa6uOhh+JccUWA7Oz9lwJEIgZ1dSbV1UHGjYtjWfFM\nlfzeey3691fFW0RERI4tBw3bNTU1h/UJvzwWUI4vpmlyzjkBamuTGAb88IcW48a5YbqqKsbcucHM\ne7t0sZkwIbTfTO+zzrI491wfa9e6vd3aOl5ERETaMqUYOaK+XPkuKbF5/fU4n3+eYscOA7/fnWBS\nVRXj5JMPXPmeMCHEgw82Mm5cFp06pVmwwCIQcGjfHk4+OazgLSIiIm2Kkos0G9M06dIlRJcu7iLL\nFSsSNDTY5OebFBX5My0nANOmxbnllgB+P/zxjz6ysmyuvTbBxRdnkZ9vs3RplL//PYrPB4EA9OkT\nwufztfAVioiIiHwzhW05KkzTpHPnYJPnzjnHZMWKBB99lGTSpBB+P9x8s8WttwaYOjXBDTe4G+lM\nnhxn9Wov1dXu8XffHcWyLPz+GB07eiktDajPW0RERFolhW1pMXsDeEmJl6VL4/z+9x7uvdfPL34R\np6HBfc+YMUnWr/dQU/PlkYIm11wTBuCXv7To2jVCTo6JaRrk5mqRpYiIiLQeCtvS4rxeLz/4gUm3\nbkkqK22Kivx89FGMJUssams9Td47ZkyS6upgZmHljTeGMiMFb7rJoqgoyYYNSb73vZD6u0VERKTF\nKY1Iq/DVkYKnnOKjW7cEXbrEqKsz6NTJpro6+I0jBefN2xe8773X4l//1cS2bT74IAFosomIiIgc\nfUoe0mr5/X6GDPFj2zZbtsQZPryReNzm5JPTzJvnjhScOTPG7Nn7esH3Bu8JE0K8/nqct9+2uemm\nAD/+cZJRo6Lk55vk5nopK1OriYiIiDQ/hW1p9dyqd4hOnSCVStGuXYxnnmkkGoUPPzSbjBT88izv\nnTvT3HRTkKuuSnDPPX4KCx2qq4Pk59ssWdJI+/Zw4oleiou1wFJERESah8K2tCler5c+fbIBd5xg\nt24Jhg+3iETSbN++L3g/+GCMUMhg1KgUCxcGGT3a7fVOJGD8+CRjx2YBsHixRXHxHgIBk4ICTTYR\nERGRI0thW9os0zQpKwtSVuYG761bE7z0UozcXPd527Y591yLmhp/5pgxY5IsWhTILLCcMmVfn/fC\nhRa9e+8hkTDxemHAgKBmeYuIiMh3ohKeHBP2Bu9+/cJ07hzENE28Xi9nnx3k17+2eO01DwsWWN+4\nwHLBggAbNni44IIszjsvi9/+NsGaNfW89VaEZDLZAlclIiIibZ0q23JM8/l8nHeeh/7949TVpSgu\nTjN0aJopU9wFll/u8x41KsXkyeFM1buyMsRvfhOhvj5NLBYjGIyp2i0iIiKHRWFbjnl7t43v1CnA\n1q0JIpEkTz3VyKZNBvE4mT7vf/7nZJOWk/x8m927DSIRD1dd5Ybzu++O0q1bDDDJz9cGOiIiIvLN\nFLbluLG31QTcfu7S0gSRSIo//KGRXbvg888dliyxqKx0g/Wtt1rU1ZncdFPoK7tXupXwxYujdO4c\nJ5UyCAQgGDTp1SuoWd4iIiKSoVQgx6W9W8XvtXeBZTwe5/HHG9mwweTttz0UF+/r8f7q7pVTpoS5\n444okye7W8fPnWvx2WdR8vIMAgGTXr0CCt4iIiLHOSUBEZpWvTt3TpKTE2fgwDQ7dsCiRRbTpoUO\nuLhyxQpvJnzPmuVONnn9dQ8LFsSor4+SleWQn++jrCyodhMREZHjkMK2yFf4fD6GDHEXQSaTSd57\nL8ry5Y04jtNk98oFCywWLgx85ViHa69NcPHF++Z4d+iQZNOmNGVlHjp1UugWERE5nihsi3wDn8/H\nwIF5gNtqUlaW4PTTLRob03z+ucHVVyeornbD88yZMUIhhxtuCGWq3XPmBJg1C6ZO3Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bf8c50>"
+       "<matplotlib.figure.Figure at 0x7ca81d0>"
       ]
      },
      "metadata": {},
@@ -2014,9 +2016,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEWCAYAAABL+3CVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b8e320>"
+       "<matplotlib.figure.Figure at 0x7cc2c18>"
       ]
      },
      "metadata": {},
@@ -2055,7 +2057,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97119e8>"
+       "<matplotlib.figure.Figure at 0x99b4898>"
       ]
      },
      "metadata": {},
@@ -2087,7 +2089,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ad12e8>"
+       "<matplotlib.figure.Figure at 0x7f98080>"
       ]
      },
      "metadata": {},
@@ -2454,7 +2456,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bbff28>"
+       "<matplotlib.figure.Figure at 0x7ff7710>"
       ]
      },
      "metadata": {},
@@ -2464,7 +2466,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8715e48>"
+       "<matplotlib.figure.Figure at 0x952df28>"
       ]
      },
      "metadata": {},
@@ -2474,7 +2476,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90ae198>"
+       "<matplotlib.figure.Figure at 0x952ec88>"
       ]
      },
      "metadata": {},
@@ -2561,7 +2563,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c62e10>"
+       "<matplotlib.figure.Figure at 0x9476e80>"
       ]
      },
      "metadata": {},
@@ -2571,7 +2573,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97ab1d0>"
+       "<matplotlib.figure.Figure at 0xa27ca90>"
       ]
      },
      "metadata": {},
@@ -2581,7 +2583,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9748e48>"
+       "<matplotlib.figure.Figure at 0xa23a4e0>"
       ]
      },
      "metadata": {},
@@ -2841,7 +2843,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bdd9b0>"
+       "<matplotlib.figure.Figure at 0x948ffd0>"
       ]
      },
      "metadata": {},
@@ -3269,9 +3271,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ac1cf8>"
+       "<matplotlib.figure.Figure at 0x9566a58>"
       ]
      },
      "metadata": {},
@@ -3359,9 +3361,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c9c048>"
+       "<matplotlib.figure.Figure at 0x7f2d240>"
       ]
      },
      "metadata": {},
@@ -3399,9 +3401,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b07cf8>"
+       "<matplotlib.figure.Figure at 0x800e9b0>"
       ]
      },
      "metadata": {},
@@ -3477,7 +3479,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

code/book_plots.py

@@ -86,7 +86,7 @@ def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
     if zs is not None:
         plot_measurements(zs)
     show_legend()
-    set_labels(title=title, x='meters', y='time (sec)')
+    set_labels(title=title, y='meters', x='time (sec)')
     if aspect_equal:
         plt.gca().set_aspect('equal')
     plt.xlim((-1, len(xs)))

code/ukf_internal.py

@@ -327,6 +327,8 @@ def plot_scatter_of_bearing_error():
         xs.append(d*math.cos(a))
         ys.append(d*math.sin(a))
     plt.scatter(xs, ys)
+    plt.xlabel('x')
+    plt.ylabel('y')
 
 
 def plot_scatter_moving_target():