I was multiply the noise generated by randn() by dt, which makes

no sense.

06-Multivariate-Kalman-Filters.ipynb

@@ -396,7 +396,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAADlCAYAAAA1IsN7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc3a7bc50>"
+       "<matplotlib.figure.Figure at 0x229cf9babe0>"
       ]
      },
      "metadata": {},
@@ -503,7 +503,7 @@
     "    p_std = math.sqrt(process_var)\n",
     "    xs, zs = [], []\n",
     "    for _ in range(count):\n",
-    "        v = vel + (randn() * p_std * dt)\n",
+    "        v = vel + (randn() * p_std)\n",
     "        x += v*dt        \n",
     "        xs.append(x)\n",
     "        zs.append(x + randn() * z_std)        \n",
@@ -1071,7 +1071,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAEWCAYAAAA97QBbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6abeeb8>"
+       "<matplotlib.figure.Figure at 0x229d2a10dd8>"
       ]
      },
      "metadata": {},
@@ -1579,9 +1579,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6b3ecc0>"
+       "<matplotlib.figure.Figure at 0x229d2a10908>"
       ]
      },
      "metadata": {},
@@ -1591,7 +1591,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7bd0828>"
+       "<matplotlib.figure.Figure at 0x229d3b36b00>"
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      },
      "metadata": {},
@@ -1632,7 +1632,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7ce77b8>"
+       "<matplotlib.figure.Figure at 0x229d3c0d5f8>"
       ]
      },
      "metadata": {},
@@ -1768,7 +1768,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6b28cf8>"
+       "<matplotlib.figure.Figure at 0x229d2197be0>"
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      },
      "metadata": {},
@@ -1818,7 +1818,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7b9e6d8>"
+       "<matplotlib.figure.Figure at 0x229d3ad45f8>"
       ]
      },
      "metadata": {},
@@ -2009,7 +2009,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc637a710>"
+       "<matplotlib.figure.Figure at 0x229d1c6eeb8>"
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      },
      "metadata": {},
@@ -2142,7 +2142,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7b3b048>"
+       "<matplotlib.figure.Figure at 0x229d3ad42e8>"
       ]
      },
      "metadata": {},
@@ -2233,7 +2233,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc633ed30>"
+       "<matplotlib.figure.Figure at 0x229d21df710>"
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      },
      "metadata": {},
@@ -2262,7 +2262,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7e002b0>"
+       "<matplotlib.figure.Figure at 0x229d21dfa20>"
       ]
      },
      "metadata": {},
@@ -2365,7 +2365,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7e000b8>"
+       "<matplotlib.figure.Figure at 0x229d3f840b8>"
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      "metadata": {},
@@ -2375,7 +2375,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7e152e8>"
+       "<matplotlib.figure.Figure at 0x229d3c84b70>"
       ]
      },
      "metadata": {},
@@ -2420,7 +2420,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7ff15c0>"
+       "<matplotlib.figure.Figure at 0x229d3f86160>"
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      "metadata": {},
@@ -2450,7 +2450,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dcbaced68>"
+       "<matplotlib.figure.Figure at 0x229d3a699b0>"
       ]
      },
      "metadata": {},
@@ -2481,7 +2481,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc5d6ef28>"
+       "<matplotlib.figure.Figure at 0x229d2a7d978>"
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@@ -2523,7 +2523,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7cfb668>"
+       "<matplotlib.figure.Figure at 0x229d219fc88>"
       ]
      },
      "metadata": {},
@@ -2533,7 +2533,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7dfcc18>"
+       "<matplotlib.figure.Figure at 0x229d3befc88>"
       ]
      },
      "metadata": {},
@@ -2578,7 +2578,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc625ea20>"
+       "<matplotlib.figure.Figure at 0x229d3bfb240>"
       ]
      },
      "metadata": {},
@@ -2588,7 +2588,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6ae6908>"
+       "<matplotlib.figure.Figure at 0x229d3c2c0b8>"
       ]
      },
      "metadata": {},
@@ -2622,7 +2622,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc5d4f2e8>"
+       "<matplotlib.figure.Figure at 0x229d2a2a0f0>"
       ]
      },
      "metadata": {},
@@ -2655,7 +2655,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc637a198>"
+       "<matplotlib.figure.Figure at 0x229d3b92630>"
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      },
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@@ -2689,7 +2689,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7edb4e0>"
+       "<matplotlib.figure.Figure at 0x229d3b926d8>"
       ]
      },
      "metadata": {},
@@ -2772,7 +2772,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6af19e8>"
+       "<matplotlib.figure.Figure at 0x229d3ace550>"
       ]
      },
      "metadata": {},
@@ -2898,7 +2898,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7ce0ac8>"
+       "<matplotlib.figure.Figure at 0x229d3b6cdd8>"
       ]
      },
      "metadata": {},
@@ -2943,7 +2943,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc7bcbe48>"
+       "<matplotlib.figure.Figure at 0x229d3c42550>"
       ]
      },
      "metadata": {},
@@ -2985,7 +2985,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dc6b15f60>"
+       "<matplotlib.figure.Figure at 0x229d219f7b8>"
       ]
      },
      "metadata": {},
@@ -3044,7 +3044,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x18dcb6e0470>"
+       "<matplotlib.figure.Figure at 0x229d3b14dd8>"
       ]
      },
      "metadata": {},