diff --git a/Chapter05_Kalman_Filters/Kalman_Filters.ipynb b/Chapter05_Kalman_Filters/Kalman_Filters.ipynb
index 27e91f0..3bce71a 100644
--- a/Chapter05_Kalman_Filters/Kalman_Filters.ipynb
+++ b/Chapter05_Kalman_Filters/Kalman_Filters.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:ce50ae76601ee29b27998c3d04ed65d7a05c21cf63a83e520527e96fff2359e3"
+ "signature": "sha256:6bbf3b69472f466f37cf43314d0391a512ece3f8f9592b3b02ec292d425f2c17"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -1227,6 +1227,31 @@
"> Before I go on, I want to emphasize that this code fully implements a 1D Kalman filter. If you have tried to read the literature, you are perhaps surprised, because this looks nothing like the complex, endless pages of math in those books. To be fair, the math gets a bit more complicated in multiple dimensions, but not by much. So long as we worry about *using* the equations rather than *deriving* them we can create Kalman filters without a lot of effort. Moreover, I hope you'll agree that you have a decent intuitive grasp of what is happening. We represent our beliefs with Gaussians, and our beliefs get better over time because more measurement means more data to work with. \"Measure twice, cut once!\""
]
},
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Animating the Tracking"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you are reading this in IPython Notebook you will be able to see an animation of the filter tracking the dog directly below this sentence.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The top plot shows the output of the filter in green, and the measurements with a dashed red line. The bottom plot shows the Gaussian at each step. \n",
+ "\n",
+ "When the track first starts you can see that the measurements varies quite a bit from the initial prediction. At this point the Gaussian probability is small (the curve is low and wide) so the filter does not trust its prediction. As a result, the filter adjusts its estimate a large amount. As the filter innovates you can see that as the Gaussian becomes taller, indicating greater certainty in the estimate, the filter's output becomes very close to a straight line. At `x=15` and greater you can see that there is a large amount of noise in the measurement, but the filter does not react much to it compared to how much it changed for the firs noisy measurement."
+ ]
+ },
{
"cell_type": "heading",
"level": 2,
diff --git a/Chapter05_Kalman_Filters/Kalman_Filters_Animations.ipynb b/Chapter05_Kalman_Filters/Kalman_Filters_Animations.ipynb
index bac67d5..df974e0 100644
--- a/Chapter05_Kalman_Filters/Kalman_Filters_Animations.ipynb
+++ b/Chapter05_Kalman_Filters/Kalman_Filters_Animations.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:e4bf6588bd5504bdeff5802ced00bcc98d4cad46767b3d4f649b0fe63a2976cf"
+ "signature": "sha256:93eea406145c65de7a62ec1f5ec3c4d26d16c2116b86fa6bd9e80589122ce2c7"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -241,13 +241,13 @@
],
"metadata": {},
"output_type": "pyout",
- "prompt_number": 2,
+ "prompt_number": 64,
"text": [
- ""
+ ""
]
}
],
- "prompt_number": 2
+ "prompt_number": 64
},
{
"cell_type": "code",
@@ -274,7 +274,7 @@
"language": "python",
"metadata": {},
"outputs": [],
- "prompt_number": 3
+ "prompt_number": 65
},
{
"cell_type": "code",
@@ -341,7 +341,7 @@
" plt.tight_layout()\n",
" \n",
"N=12\n",
- "animate('volt_animate.gif', volt_animate, N*3, 500)"
+ "animate('volt_animate.gif', volt_animate, N*3, 350)"
],
"language": "python",
"metadata": {},
@@ -349,13 +349,13 @@
{
"metadata": {},
"output_type": "display_data",
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r4+Pj/VFGr2A2m4Hub5PzzrOxdavr62lUVDzRsbEAODpRR3099O9vbjqW4grK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\n9Stzc3MqylHKSwvtRqNRYrEY69cvX33jzZs39PT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07tA062LLFoVmLnWlcE5GwtPfHL6S3xScAsC4ccMfxPPrX5dy+eU9dzepqOjU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\nvGbfOsaNb+a1vc1UvLZ3iP/3YGWk2Sb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"text": [
- ""
+ ""
]
}
],
- "prompt_number": 38
+ "prompt_number": 69
},
{
"cell_type": "markdown",
@@ -368,164 +368,63 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "class DogSensor(object):\n",
- " \n",
- " def __init__(self, x0=0, velocity=1, noise=0.0):\n",
- " \"\"\" x0 - initial position\n",
- " velocity - (+=right, -=left)\n",
- " noise - scaling factor for noise, 0== no noise\n",
- " \"\"\"\n",
- " self.x = x0\n",
- " self.velocity = velocity\n",
- " self.noise = math.sqrt(noise)\n",
- "\n",
- " def sense(self):\n",
- " self.x = self.x + self.velocity\n",
- " return self.x + random.randn() * self.noise\n",
- " \n",
- " \n",
"# assume dog is always moving 1m to the right\n",
"movement = 1\n",
- "movement_error = .2\n",
+ "movement_error = .05\n",
"sensor_error = 4.5\n",
- "pos = (0, 100) # gaussian N(0,50)\n",
- "\n",
- "dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n",
+ "pos = (0, 100) # gaussian N(0,100)\n",
"\n",
+ "# this is the recorded output of a run of the dog sensor with an initial\n",
+ "# seed of 200\n",
+ "ZS = [-2.07, 6.05, 4.51, 3.47, 5.76, 5.93, 6.53, 9.01, 7.53, 11.68, \n",
+ " 11.15, 14.76, 13.45, 16.15, 19.05, 14.87, 20.90, 15.75, \n",
+ " 17.16, 20.50]\n",
"zs = []\n",
"ps = []\n",
- "vs = []\n",
+ "\n",
+ "N=20\n",
"\n",
"def dog_animate(frame):\n",
- " global pos, zs, ps, vs\n",
+ " global pos, zs, ps, N, ZS\n",
" pos = predict(pos[0], pos[1], movement, movement_error) \n",
- " Z = dog.sense()\n",
+ " Z = ZS[frame]\n",
" zs.append(Z)\n",
- " vs.append(pos[1])\n",
- " print(pos[1])\n",
" \n",
" pos = update(pos[0], pos[1], Z, sensor_error)\n",
" ps.append(pos[0])\n",
"\n",
+ "\n",
" plt.subplot(211)\n",
- " plt.scatter (frame, Z)\n",
+ " plt.plot(zs,c='r', linestyle='dashed')\n",
+ "\n",
+ " plt.xlim([0,N*1.2])\n",
+ " plt.ylim([0,N*1.2])\n",
+ "\n",
+ " if len(ps) > 1:\n",
+ " plt.plot(ps, c='#8EBA42')\n",
+ " \n",
" plt.subplot(212)\n",
" plt.cla()\n",
- " stats.plot_gaussian(pos[0], pos[1], xlim=[0,32])\n",
+ " stats.plot_gaussian(pos[0], pos[1], xlim=[0,N*1.2])\n",
" plt.ylim([0,1])\n",
+ " plt.tight_layout()\n",
"\n",
"\n",
- "N=12\n",
"animate('dog_track.gif', dog_animate, N, 200)"
],
"language": "python",
"metadata": {},
"outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "100.2\n",
- "4.506590257879656"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "2.451646358922152"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.7870209797122434"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.4790786661369082"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.3131905715360732"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.2165428948514319"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.1576492519214656"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.1207749370248357"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.0972939271041793"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.0821803421932217"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1.072385204587669"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- },
{
"metadata": {},
"output_type": "display_data",
- "png": 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Pffv2YdSoUT0ujoiI7FOXlyKvXbsW+fn5iIqKQkZGBjZu3Njq9fLy8jZJuXLl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\nHgVnJ2n/yi2aPRYA8LeDeWg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IkVw87jfExsbCU1B00l9weHDfZcrZZ7vCufDll71SvoiIOxSc0mcjr7qKstFD\nWH39BHadM7LdujGxZ3HmuO90+EyXoVldDbGxna/LycG0vrQAZ57pcc0iIp5ScEqf/HvHXVQtPRus\nBWNcy4dHTWfBxLvd3l/K1KkYoOzHP6bh9tvbrRt+001Aa29z6dK+lC0i4jEFp3hk2e6HKK3ffnRB\na2jGRYzl4snuDfZpE/PAAxjAAIm//CWOJ56gaM8e1/qS1asJff55En/7W5jd/e0sIiK+EuLvAmRg\n+Xjfb1mcc1370ASiQ5K4NuN5j0MToOa++2hKScHiDM/QhgZS09Jg5dEBRI7rrqP4P//x+BgiIn2l\nHqf0yqf5L7Kz4t8dloeZWK6e8XuvHadkwwZYuZLUr34VcAZo6le/SkF+vteOISLSFwpO6da2onfY\nVPz3dstGba0gfXUJo59Y45uDzptHQX4+IydMILShgYaZM31zHBERDyg4pVN7y9ew9tDv2i+0lsvv\n28ykT0qxQME3P/PptcaiPXu6H2UrIuIHCk5p560d/0tl0/6OKxwt3HbxCkJxjmptTE/vnwE6Ck0R\nCTAKTuFI5UHezvtpl+svGfs7Jk+bRgjO0HRERFD6UdeP/xIRCWYKzkHsnZ33UtG4t8v1F4/5BXFx\no4m/+up2Ew8U7e36MyIiwU7BOcj01LtMjJzMBZPubbesYskSItLTCWlspFCjW0VkkFNwDhK97V12\nRb1MEREnBWcQq6w8yFtu9i5FRKR7Cs4gtHj97ZTW7uxyfXe9S/P004x88EEaTjuNildf9VWJIiID\nloIzSBw5coS3D97a5fqEyIl8YdL9HVdUVjL8zDMJKy93DQAyQNTatT6oUkRk4FNwDnD/yXuGfZXL\nu1zf07XLlOnTO0xYbL1Um4hIMFJwDlAv53yLFho6XZcQlc4XJj54dEFlJcNuvpkjL73UYVtrDNY6\no9ICdeec0+l2IiLipOAcQPZXrOWT/P/rcv0Zo3/A7AkLAGi88kpi3noLc8z6I5185vC770JcHIzu\nulcqIiJHDejgHPrFL1I7cyaOhx7ydyk+9eb2n1DdfKj9QmvBQmhIFNdk/Pno8htuIOKFF4iAdqFp\nAZ57Dr75zfb7mTHDFyWLiAStgRucu3YRu349sevX0/y3v1F84IC/K/KqYwf7mBYLIc4YTDhQw+zX\nDjJ9WSGNp51N5Qt/bv/B3/wGXngBcIalBRxDh1K8dq2zZykiIn0yYIMz7pZbAGevKszhICUtjcKV\nK2HSJP8QeCk5AAAgAElEQVQW5oHoW28l/tVXKbnpJj66cWzHwT4tlskfFnHi6/mkbalwLba5uR13\nFhdH89ChVNx4I00/+pGPKxcRGXwGbHBWvvMOlY89Rurjj2NofeDxvHnUTp7MkRUr/Fxd75nHHyf+\n1VcpHz2ENaPWsK9yd4dtLrt/C+PXl9MSG0vNmWdSee+90M0zKluKi2kqLfVl2SIig9aACs7ktDRC\ngBbgcH4+/PCHFPzwh67lBojeubPTQTAB6b334I2neeuuGWw/N9l1OrbNGaN/wNhhJ8KrUOSnEkVE\npD2vB6cxxgFsbn270lp7h7f23RaO5rjlh/PzSTj1VKIOHaLwD3/w1uF86uV11zGqqJyDT5ziDMzW\nW0JCCOfLGc/4uToREemKL3qctdZanz7huKWTZeXr1vnykF6TnXMDliaIthw8MYGQphZii+ppHDWa\nL07/jb/LExGRHgyYU7UJJ52EofV5kE895dZn4886i4o33oDERJ/U1hvZOddjaW63bMLHxUz94BBx\nL23u4lMiIhJofBGcUcaYDUAd8FNr7YfHb5CUlOT2TsMPHz76+Rtv7P0HzzmHiH37iJ45k8aLLoJ/\n/cvtY/fFH1ZdQctxgQkQG5nKRT99C7p+eIlHwsPDAc/aWHpHbex7amPfUxt7zhfBmWatLTLGnAK8\naoyZZK1tNzfcokWLXK/nzp3LvHnzetxp23VNd+dRDWs9hWuAiLffxhEVhaO+3s29uO/3q67o0MME\nGBY5mq/N+aPPjy8iIr23cuVKVq1a5Xo/f/78Lrc1bfOU+oIx5j/AN6y129uWvf/++3b69Olu7yv8\nzjtJzM6mafhwyjZtcuuzSVOnElFd7TrVa4HCf/wDzj3X7Tp6sjjnm4Cjw/KhjiQuOcH31zDbfnos\n1e0oPqM29j21se+pjbuXm5vLggULjh+LCni5x2mMSQDqrbV1xpjxQBrglSl9mh5/nMOPP+7RZ0u3\nb4fFi0n9/vcB5+jckV/7GkX5+d4oDYDFOf9NZ8OW4sLH8M35fyUEqLy8npoBMupXREQ65+1TtdOA\nZ40xDTi7XTdaa+u8fAzPXHstBddeS3JaGga8Epob8p9nV8W7na4bFjGeiyYvYuSYMa7baIa+8YaC\nU0RkgPNqcFpr1+AMz4B1uI+BuXT7j6hpLuxyfXzEBC6c/AAAIyZOJLSlxXWKuPDjj/t0bBER8b8B\ncztKf0hNS8MC5V/5Cg2/+hUA5eXlvHvoTjq7bnmsuIixXDz5Z673SbNmEVZf7wrNgpdfhvHjfVW6\niIj0k4APzpj/+i9awsKoe/55nx5n+JQprlmJhr2ymM21H/LxDRNpHtJ1E8WGjeLSqY90ui6ipORo\naC5aBGee6YuyRUSknwV8cA5dtcoZZmlpFHpxMM/xXvnn1xi24iMaY8I4lBFPY0znTTM14TJOHHVt\nj/srePNNUi69lIqvfAVuuMHb5YqIiJ8EfHC29QJ9cdPMKznfwkGD60CH56e41g07VMvUFUUk7a3G\n8dQSEhImuLfzE0/0adCLiIh/BHZw/ulPrpd1kyd7bbfv7XqIsobtXa4PJ4aLLniekd9Mo2HmTI64\nG5oiIhK0Ajo4k++/39Xb9MYzNsvKyniv4PbOjxU9k3Mn/Ljdsi5vWSkvJyUzk8aUFMo2bICNG4l8\n6SUaHn64zzWKiEhgC+jgDPHivl7JuQkH7afaM4SRlfGs2/tKzswkBIgsLCQ1Lc21vDYnhyNvvNHX\nUkVEJIAFdHC2Xdfs7DFivbWrbA0bCn7XYfkFqfeTmDjRo306YmIIqakB2s+hG7lxo4dViojIQBHQ\nwVmYnw+lpbBjh0efX5xzXYdlsWGjuXTqL/pUV0lrPcOnTCG8NUBbgKK8vD7tV0REAl9ABycASUlw\nxhlufWTl3l9TWPtZh+XXZnj3XtASDwNdREQGrsAPTjd0NfhnWsIXmTXqGj9UJCIiwSZogvPVnFto\npPK4paFcm/GcP8oREZEgNeCD80DZRtYU/KrD8gtS7yMxcZIfKhIRkWAWkME5Mi2NEKBp6FBKt23r\ncrvFOd/g+DmFokNHcMW0X/u2QBERGbQCMjhDcd7mEV5V1en6j/Y9RX7NJx2We3vwj4iIyPECLzhL\nS10vHcZ0WP3v7XdT1dz+to9xQ+dy+tibfF6aiIhIwAVn0umnu6bZK3755Q7r24em4dqMv/VXaSIi\nIl6d1c4rwmtrj7457v7NZbvbT1yg0BQRkf4WcMF57BR2xyut/9z1WtczRUTEHwIuOB04Q7Np6NB2\ny/+T92e/1CMiInKsgLvG2dWjvPZVrnS9Pj/lN/1VjoiISDsB1+PsTM7hf7d7n5SU5KdKRERksBsQ\nwbm15EXXa/U2RUTEnwI+OPeXftruvXqbIiLiT4ETnMdMfHCsTwofd71ekHJPf1UjIiLSqYAZHJRy\nwgkYnA+EPtw6QKi0dGe7bYYnTen/wkRERI4RMD1Ow9F7ONssK3zQ9fqklG/3az0iIiKdCYjgDL/z\nTsB5/2b13LkAlB536nZy0tn9XZaIiEgHARGcidnZrt5mzYvOEbTLCu9wrZ8Sf5kfqhIREekoIIKz\npyJmp13bL3WIiIj0JCCCs01L6++Lc65zLUuLmeOfYkRERDoREMFZsHkzDUOGcPj++zusO3v8rf1f\nkIiISBcC43aUpCRKdzpvPTm2txkfMclfFYmIiHTK6z1OY0yWMWaHMWa7MaZPo3ounHyft8oaVHJz\nc/1dQtBTG/ue2tj31Mae8WpwGmMigIeBs4DzAbcmll2c89+u10NCk71Z2qCifwy+pzb2PbWx76mN\nPePtHuccIMdaW2ytzQPyjDGzev/xFtery6c95uXSRERE+s7b1ziTgQJjzLeBMqAQSAU2HbtR20Tt\nIbGxYC0tn33G04V3udZHhMRpMncPhYeHc9555xEfH+/vUoKW2tj31Ma+pzb2nE8GB1lrnwYwxlyF\nc0Kgdj766CPni7ffdv5eVERGyPc730ZERCSAeDs4C3D2MNuktC5zWbBgwfFT0oqIiAwY3g7OdUCG\nMWYEEAWMttZu9vIxRERE/MarwWmtbTTG3AV83Lroju62FxERGWiMtR0uQYqIiEgXAmLKPRERkYFC\nwSkiIuKGfp2rNisrKwt4COctKj/Izs5e2p/HHwyysrIcQNuArJXZ2dm6ztxHWVlZjwFfB4qzs7Nn\nti7Td9mLumhjfZe9KCsrKw1YDMQDDcBPsrOzl+m77L5+C86srKy26fjm4BxxuxzQX5D31WZnZ8/2\ndxFBZgnwIvAc6LvsI+3auJW+y97VBHw3Ozt7S1ZW1lhgdVZWVjr6LrutP0/VzgFysrOzi7Ozs/OA\nvKysLDem4xPxj+zs7DVA6TGL9F32sk7aWLwsOzu7KDs7e0vr6wNABHAG+i67rT9P1SYDBVlZWd1O\nxyd9FpWVlbUBqAN+mp2d/aG/CwpCKei73B/0XfaRrKysC4ENwEj0XXZbvw8Oys7Ofjo7O/vl1re6\nF8b70rKzs0/GeQ/tC1lZWZH+LihY6bvsc/ou+0BWVlYK8BiwsG2Zvsvu6c/g7HE6Pum77Ozsotbf\n1wOHgPF+LSg4HULfZZ/Td9n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"text": [
- ""
+ ""
]
}
],
- "prompt_number": 49
+ "prompt_number": 73
},
{
"cell_type": "markdown",
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new file mode 100644
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diff --git a/Chapter05_Kalman_Filters/volt_animate.gif b/Chapter05_Kalman_Filters/volt_animate.gif
index 4b1c227..a02b993 100644
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