Made sensor fusion code a function.

The function takes the wheel and gps variance in as parameters
so I can easily vary the values and display them to the reader.

08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:99ce1d42001f74032bcccc6919adcf581e234259ab9db1c2aa8da6551ff2c728"
+  "signature": "sha256:7abdbb6792be112481394d0099141b4a4c633b66c486d753cbcc2f6d854636d7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f7a9b19c5c0>"
+        "<IPython.core.display.HTML at 0x7fe3077d45f8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a7e5d63c8>"
+        "<matplotlib.figure.Figure at 0x7fe2eac40400>"
        ]
       }
      ],
@@ -707,9 +707,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a7e21df98>"
+        "<matplotlib.figure.Figure at 0x7fe2ea87cf98>"
        ]
       }
      ],
@@ -797,9 +797,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a7df5ffd0>"
+        "<matplotlib.figure.Figure at 0x7fe3077c9908>"
        ]
       },
       {
@@ -884,7 +884,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a7e207908>"
+        "<matplotlib.figure.Figure at 0x7fe2ea871710>"
        ]
       }
      ],
@@ -957,48 +957,50 @@
       "from filterpy.kalman import KalmanFilter\n",
       "from numpy import array, asarray\n",
       "\n",
-      "dt = 0.1\n",
-      "wheel_sigma = 1.5\n",
-      "gps_sigma = wheel_sigma*2\n",
-      "kf = KalmanFilter(dim_x=2, dim_z=2)\n",
+      "def fusion_test(wheel_sigma, gps_sigma, do_plot=True):\n",
+      "    dt = 0.1\n",
+      "    kf = KalmanFilter(dim_x=2, dim_z=2)\n",
       "\n",
-      "kf.F = array ([[1., dt], [0., 1.]])\n",
-      "kf.H = array ([[1., 0.], [1., 0.]])\n",
-      "kf.X = array ([[0.], [1.]])\n",
-      "kf.Q *= 0.01\n",
-      "kf.P *= 100\n",
-      "kf.R[0,0] = wheel_sigma**2\n",
-      "kf.R[1,1] = gps_sigma**2 \n",
+      "    kf.F = array ([[1., dt], [0., 1.]])\n",
+      "    kf.H = array ([[1., 0.], [1., 0.]])\n",
+      "    kf.X = array ([[0.], [1.]])\n",
+      "    kf.Q *= array([[(dt**3)/3, (dt**2)/2],\n",
+      "                   [(dt**2)/2,  dt      ]]) * 0.02\n",
+      "    kf.P *= 100\n",
+      "    kf.R[0,0] = wheel_sigma**2\n",
+      "    kf.R[1,1] = gps_sigma**2 \n",
       "\n",
-      "random.seed(1123)\n",
-      "xs = []\n",
-      "zs = []\n",
-      "nom = []\n",
-      "for i in range(1, 100):\n",
-      "    m0 = i + randn()*wheel_sigma\n",
-      "    m1 = i + randn()*gps_sigma\n",
-      "    z = array([[m0], [m1]])\n",
+      "    random.seed(1123)\n",
+      "    xs = []\n",
+      "    zs = []\n",
+      "    nom = []\n",
+      "    for i in range(1, 100):\n",
+      "        m0 = i + randn()*wheel_sigma\n",
+      "        m1 = i + randn()*gps_sigma\n",
+      "        z = array([[m0], [m1]])\n",
       "\n",
-      "    kf.predict()\n",
-      "    kf.update(z)\n",
+      "        kf.predict()\n",
+      "        kf.update(z)\n",
       "\n",
-      "    xs.append(kf.X.T[0])\n",
-      "    zs.append(z.T[0])\n",
-      "    nom.append(i)\n",
+      "        xs.append(kf.X.T[0])\n",
+      "        zs.append(z.T[0])\n",
+      "        nom.append(i)\n",
       "\n",
-      "xs = asarray(xs)\n",
-      "zs = asarray(zs)\n",
-      "nom = asarray(nom)\n",
+      "    xs = asarray(xs)\n",
+      "    zs = asarray(zs)\n",
+      "    nom = asarray(nom)\n",
       "\n",
       "\n",
-      "res = nom-xs[:,0]\n",
-      "print('fusion std: {:.3f}'.format (np.std(res)))\n",
+      "    res = nom-xs[:,0]\n",
+      "    print('fusion std: {:.3f}'.format (np.std(res)))\n",
+      "    if do_plot:\n",
+      "        plt.plot(xs[:,0], label='Kalman filter')\n",
+      "        plt.plot(zs[:,0],linestyle=':', label='wheel')\n",
+      "        plt.plot(zs[:,1],linestyle=':', label='GPS')\n",
+      "        plt.legend(loc=4)\n",
+      "        plt.show()\n",
       "\n",
-      "plt.plot(xs[:,0], label='Kalman filter')\n",
-      "plt.plot(zs[:,0],linestyle=':', label='wheel')\n",
-      "plt.plot(zs[:,1],linestyle=':', label='GPS')\n",
-      "plt.legend(loc=4)\n",
-      "plt.show()"
+      "fusion_test(1.5, 3.0)"
      ],
      "language": "python",
      "metadata": {},
@@ -1007,15 +1009,15 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "fusion std: 0.422\n"
+        "fusion std: 0.391\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a769411d0>"
+        "<matplotlib.figure.Figure at 0x7fe2e2fa3ac8>"
        ]
       }
      ],
@@ -1033,13 +1035,35 @@
       "\n",
       "##### Solution 1\n",
       "\n",
-      "Force the Kalman filter to disregard the GPS measurement by setting the measurement noise for the GPS to a near infinite value. Re-run the filter and observe the standard deviation of the residual. \n",
-      "\n",
-      "For example, if I edit the cell above with\n",
-      "\n",
-      "    kf.R[1,1] = 1.e80\n",
-      "\n",
-      "and run the cell again I get a standard deviation of 0.494, vs the standard deviation of 0.422 when the GPS measurement is used. \n",
+      "Force the Kalman filter to disregard the GPS measurement by setting the measurement noise for the GPS to a near infinite value. Re-run the filter and observe the standard deviation of the residual. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fusion_test(1.5, 3.0, do_plot=False)\n",
+      "fusion_test(1.5, 1e80, do_plot=False)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "fusion std: 0.391\n",
+        "fusion std: 0.438\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here we can see the error in the filter where the GPS measurement is almost entirely ignored is greater than that where it is used.\n",
       "\n",
       "\n",
       "##### Solution 2\n",
@@ -1112,11 +1136,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a768a9198>"
+        "<matplotlib.figure.Figure at 0x7fe2e2fa39e8>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1217,7 +1241,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1260,7 +1284,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a768f84a8>"
+        "<matplotlib.figure.Figure at 0x7fe2e2f9d780>"
        ]
       },
       {
@@ -1268,11 +1292,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a76900978>"
+        "<matplotlib.figure.Figure at 0x7fe2e2f9dba8>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1410,7 +1434,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1450,11 +1474,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a76784fd0>"
+        "<matplotlib.figure.Figure at 0x7fe2e2df0198>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1595,7 +1619,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1645,11 +1669,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a7e3109e8>"
+        "<matplotlib.figure.Figure at 0x7fe2e2f03be0>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1786,11 +1810,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a767aa588>"
+        "<matplotlib.figure.Figure at 0x7fe2e3026860>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1860,7 +1884,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1919,11 +1943,11 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a768d3668>"
+        "<matplotlib.figure.Figure at 0x7fe2e2f02240>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1987,7 +2011,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a88145f98>"
+        "<matplotlib.figure.Figure at 0x7fe2ea92fcf8>"
        ]
       },
       {
@@ -1995,11 +2019,11 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a768e9cc0>"
+        "<matplotlib.figure.Figure at 0x7fe2e2ddcef0>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -2025,7 +2049,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a76796668>"
+        "<matplotlib.figure.Figure at 0x7fe2e2fb7be0>"
        ]
       },
       {
@@ -2033,11 +2057,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7a767ecef0>"
+        "<matplotlib.figure.Figure at 0x7fe2e2cc9e80>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",

merge_book.py

@@ -43,17 +43,17 @@ if __name__ == '__main__':
     #merge_notebooks(sys.argv[1:])
     merge_notebooks(
         ['Preface.ipynb',
-         'Chapter01_gh_filter/g-h_filter.ipynb',
-         'Chapter02_Discrete_Bayes/discrete_bayes.ipynb',
-         'Chapter03_Least_Squares/Least_Squares_Filters.ipynb',
-         'Chapter04_Gaussians/Gaussians.ipynb',
-         'Chapter05_Kalman_Filters/Kalman_Filters.ipynb',
-         'Chapter06_Multivariate_Kalman_Filter/Multivariate_Kalman_Filters.ipynb',
-         'Chapter07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb',
-         'Chapter08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb',
-         'Chapter09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb',
-         'Chapter10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb',
-         'Chapter11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
+         '01_gh_filter/g-h_filter.ipynb',
+         '02_Discrete_Bayes/discrete_bayes.ipynb',
+         '03_Least_Squares/Least_Squares_Filters.ipynb',
+         '04_Gaussians/Gaussians.ipynb',
+         '05_Kalman_Filters/Kalman_Filters.ipynb',
+         '06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb',
+         '07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb',
+         '08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb',
+         '09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb',
+         '10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb',
+         '11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
          'Appendix_A_Installation/Appendix_Installation.ipynb',
          'Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb'])