daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Fixed sections to use header cells.

Browse Source 
Use of markdown was causing chapters to be merged together.
I made the change to markdown in some place in preparation for
IPython 3.0, but that turned out to be a bad idea (plus, 3.0
will automatically convert the headers correctly).
This commit is contained in:
Roger Labbe committed 2015-02-28 22:49:23 -08:00
1 parent 750a30bdd2
commit 7c5bdccaa1
8 files changed
+266 -215
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10_Unscented_Kalman_Filter.ipynb
+62 -42
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:5213c6c9ac5acfcd6066c0b772b3a9f400973e7dc18a2763687fddb69a34ee60"
"signature": "sha256:0513e14b11503d8a163269f3d941f916e92cbfe623575971f2bd00f42bba8101"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -16,10 +16,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"#Unscented Kalman Filters"
"Unscented Kalman Filters"
]
},
{
@@ -320,10 +321,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Choosing Sigma Points"
"Choosing Sigma Points"
]
},
{
@@ -590,10 +592,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Handling the Nonlinearities"
"Handling the Nonlinearities"
]
},
{
@@ -624,10 +627,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Sigma Point Algorithm"
"Sigma Point Algorithm"
]
},
{
@@ -664,10 +668,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Unscented Transform"
"The Unscented Transform"
]
},
{
@@ -686,10 +691,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Unscented Filter"
"The Unscented Filter"
]
},
{
@@ -700,10 +706,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"###Predict Step"
"Predict Step"
]
},
{
@@ -766,10 +773,11 @@
"prompt_number": 8
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"###Update Step"
"Update Step"
]
},
{
@@ -814,10 +822,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Using the UKF"
"Using the UKF"
]
},
{
@@ -1028,17 +1037,19 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"## Tracking a Flying Airplane"
"Tracking a Flying Airplane"
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### First Attempt"
"First Attempt"
]
},
{
@@ -1333,10 +1344,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Tracking Manuevering Aircraft"
"Tracking Manuevering Aircraft"
]
},
{
@@ -1489,10 +1501,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Sensor Fusion"
"Sensor Fusion"
]
},
{
@@ -1637,10 +1650,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Multiple Position Sensors"
"Multiple Position Sensors"
]
},
{
@@ -1837,10 +1851,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Iterative Least Squares for Sensor Fusion"
"Iterative Least Squares for Sensor Fusion"
]
},
{
@@ -1928,10 +1943,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"## Derivation of ILS Equations (Optional)"
"Derivation of ILS Equations (Optional)"
]
},
{
@@ -2058,10 +2074,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"## Implementing Iterative Least Squares"
"Implementing Iterative Least Squares"
]
},
{
@@ -2881,7 +2898,7 @@
},
{
"cell_type": "heading",
"level": 2,
"level": 3,
"metadata": {},
"source": [
"Solution"
@@ -2981,10 +2998,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Implementation of the UKF"
"Implementation of the UKF"
]
},
{
@@ -4155,10 +4173,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Choosing Values for kappa"
"Choosing Values for kappa"
]
},
{
@@ -4254,10 +4273,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Skewed Unscented Transform"
"The Skewed Unscented Transform"
]
},
{
11_Extended_Kalman_Filters.ipynb
+93 -77
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:01b730acd3b00473e0fe4652cf7023ecc7dbd6f08add98adf0b878a297e423e1"
"signature": "sha256:6ed86d974218fb521765a454a9710da68845d745e7d5d3da1117d35d7365aa4a"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -16,10 +16,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"#The Extended Kalman Filter"
"The Extended Kalman Filter"
]
},
{
@@ -256,13 +257,13 @@
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f1abad6e710>"
"<IPython.core.display.HTML at 0x7feec3d09908>"
]
}
],
"prompt_number": 2
"prompt_number": 1
},
{
"cell_type": "markdown",
@@ -283,10 +284,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Problem with Nonlinearity"
"The Problem with Nonlinearity"
]
},
{
@@ -321,10 +323,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Effect of Nonlinear Transfer Functions on Gaussians"
"The Effect of Nonlinear Transfer Functions on Gaussians"
]
},
{
@@ -353,13 +356,13 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa26a6780>"
"<matplotlib.figure.Figure at 0x7feea7874ef0>"
]
}
],
"prompt_number": 3
"prompt_number": 2
},
{
"cell_type": "markdown",
@@ -385,13 +388,13 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": "iVBORw0KGgoAAAANSUhEUgAAAyQAAAGaCAYAAAD+VYcoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa26a6668>"
"<matplotlib.figure.Figure at 0x7feea7874c88>"
]
}
],
"prompt_number": 4
"prompt_number": 3
},
{
"cell_type": "markdown",
@@ -419,13 +422,13 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAGaCAYAAACMmuWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa2298ba8>"
"<matplotlib.figure.Figure at 0x7feea71ee0b8>"
]
}
],
"prompt_number": 5
"prompt_number": 4
},
{
"cell_type": "markdown",
@@ -455,13 +458,13 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAGaCAYAAACMmuWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa0fb4b38>"
"<matplotlib.figure.Figure at 0x7feea71ee278>"
]
}
],
"prompt_number": 6
"prompt_number": 5
},
{
"cell_type": "markdown",
@@ -484,12 +487,12 @@
"output_type": "stream",
"stream": "stdout",
"text": [
"input mean, variance: -0.0001, 0.9995\n",
"output mean, variance: -0.0275, 2.2404\n"
"input mean, variance: 0.0000, 0.9979\n",
"output mean, variance: -0.0280, 2.2378\n"
]
}
],
"prompt_number": 7
"prompt_number": 6
},
{
"cell_type": "markdown",
@@ -514,20 +517,20 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": "iVBORw0KGgoAAAANSUhEUgAAAxgAAAGaCAYAAACMmuWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa2298160>"
"<matplotlib.figure.Figure at 0x7feea6f8a390>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"output mean, variance: 0.0001, 2.2489\n"
"output mean, variance: -0.0000, 2.2453\n"
]
}
],
"prompt_number": 8
"prompt_number": 7
},
{
"cell_type": "markdown",
@@ -558,12 +561,12 @@
"output_type": "stream",
"stream": "stdout",
"text": [
"linear output mean, variance: 0.0057, 7478.2709\n",
"nonlinear output mean, variance: -1.8465, 26169.1410\n"
"linear output mean, variance: -0.0025, 7466.3448\n",
"nonlinear output mean, variance: -1.8551, 26130.9999\n"
]
}
],
"prompt_number": 9
"prompt_number": 8
},
{
"cell_type": "markdown",
@@ -573,10 +576,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Extended Kalman Filter"
"The Extended Kalman Filter"
]
},
{
@@ -607,11 +611,11 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa216d6a0>"
"<matplotlib.figure.Figure at 0x7feea6046a58>"
]
}
],
"prompt_number": 10
"prompt_number": 9
},
{
"cell_type": "markdown",
@@ -642,11 +646,11 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa1179f28>"
"<matplotlib.figure.Figure at 0x7feea6f59240>"
]
}
],
"prompt_number": 11
"prompt_number": 10
},
{
"cell_type": "markdown",
@@ -683,11 +687,11 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa22421d0>"
"<matplotlib.figure.Figure at 0x7feea7099c18>"
]
}
],
"prompt_number": 12
"prompt_number": 11
},
{
"cell_type": "markdown",
@@ -723,10 +727,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Example: Tracking a Flying Airplane"
"Example: Tracking a Flying Airplane"
]
},
{
@@ -751,13 +756,13 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f1aa0fabac8>"
"<matplotlib.figure.Figure at 0x7feea607d128>"
]
}
],
"prompt_number": 13
"prompt_number": 12
},
{
"cell_type": "markdown",
@@ -799,21 +804,28 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Design the State Variables\n",
"\n",
"So we want to track the position of an aircraft assuming a constant velocity and altitude, and measurements of the slant distance to the aircraft. That means we need 3 state variables - horizontal distance, velocity, and altitude.\n",
"\n",
"$$\\mathbf{x} = \\begin{bmatrix}distance \\\\velocity\\\\ altitude\\end{bmatrix}= \\begin{bmatrix}x_{pos} \\\\x_{vel}\\\\ x_{alt}\\end{bmatrix}$$"
"Design the State Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Design the System Model"
"So we want to track the position of an aircraft assuming a constant velocity and altitude, and measurements of the slant distance to the aircraft. That means we need 3 state variables - horizontal distance, velocity, and altitude.\n",
"\n",
"$$\\mathbf{x} = \\begin{bmatrix}distance \\\\velocity\\\\ altitude\\end{bmatrix}= \\begin{bmatrix}x_{pos} \\\\x_{vel}\\\\ x_{alt}\\end{bmatrix}$$"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Design the System Model"
]
},
{
@@ -873,10 +885,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Design the Measurement Model"
"Design the Measurement Model"
]
},
{
@@ -961,7 +974,7 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
"prompt_number": 13
},
{
"cell_type": "markdown",
@@ -984,7 +997,7 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
"prompt_number": 14
},
{
"cell_type": "markdown",
@@ -1030,7 +1043,7 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
"prompt_number": 15
},
{
"cell_type": "markdown",
@@ -1146,35 +1159,36 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa218ccc0>"
"<matplotlib.figure.Figure at 0x7feea70b5128>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa1935278>"
"<matplotlib.figure.Figure at 0x7feea5ff9d68>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa10ccd68>"
"<matplotlib.figure.Figure at 0x7feea5f48860>"
]
}
],
"prompt_number": 17
"prompt_number": 16
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Using SymPy to compute Jacobians"
"Using SymPy to compute Jacobians"
]
},
{
@@ -1209,7 +1223,7 @@
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"prompt_number": 17,
"text": [
"\u23a1 x_pos x_alt \u23a4\n",
"\u23a2\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 0 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u23a5\n",
@@ -1219,7 +1233,7 @@
]
}
],
"prompt_number": 18
"prompt_number": 17
},
{
"cell_type": "markdown",
@@ -1229,10 +1243,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Designing Q"
"Designing Q"
]
},
{
@@ -1290,7 +1305,7 @@
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"prompt_number": 18,
"text": [
"\u23a1 3 2 2 2 2 \u23a4\n",
"\u23a2 \\Delta{t} \u22c5w_vel \\Delta{t} \u22c5w_vel \\Delta{t} \u22c5w_alt\u22c5w_vel\u23a5\n",
@@ -1309,13 +1324,14 @@
]
}
],
"prompt_number": 19
"prompt_number": 18
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Example: A falling Ball"
"Example: A falling Ball"
]
},
{
@@ -1338,13 +1354,13 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f1aa11ae828>"
"<matplotlib.figure.Figure at 0x7feea70e32e8>"
]
}
],
"prompt_number": 20
"prompt_number": 19
},
{
"cell_type": "markdown",
12_Designing_Nonlinear_Kalman_Filters.ipynb
+5 -5
View File
@@ -1,7 +1,7 @@
{
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"signature": "sha256:2bd183b0492298f75de42afd5f57e673cf7985d2fdbea889cc00750e85ad752c"
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},
"nbformat": 3,
"nbformat_minor": 0,
@@ -259,7 +259,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f9d97880320>"
"<IPython.core.display.HTML at 0x7f8afa59b908>"
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}
],
@@ -404,7 +404,7 @@
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f9d7b1475f8>"
"<matplotlib.figure.Figure at 0x7f8ade102d68>"
]
}
],
@@ -824,9 +824,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f9d7a114e80>"
"<matplotlib.figure.Figure at 0x7f8adce16898>"
]
}
],
13_Smoothing.ipynb
+7 -7
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:270d703c1fbe073d7501e6a1736e1bb06f2e953a07abbb6e54121b7fe2228284"
"signature": "sha256:33337ad6027b96a245419c3f7a5a7ca3631baf99b66e6905b9fde6787c3da351"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -259,7 +259,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f22d8ee6320>"
"<IPython.core.display.HTML at 0x7fba9bc01908>"
]
}
],
@@ -298,7 +298,7 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f22bc7e4a90>"
"<matplotlib.figure.Figure at 0x7fba7f74ab00>"
]
}
],
@@ -339,7 +339,7 @@
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f22bc79d048>"
"<matplotlib.figure.Figure at 0x7fba7f4c3e10>"
]
}
],
@@ -375,7 +375,7 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f22bc745e48>"
"<matplotlib.figure.Figure at 0x7fba7f4105f8>"
]
}
],
@@ -557,7 +557,7 @@
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truncated
"text": [
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"text": [
"<matplotlib.figure.Figure at 0x7f22bc7579e8>"
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]
}
],
14_Adaptive_Filtering.ipynb
+44 -44
View File
@@ -1,7 +1,7 @@
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"output_type": "pyout",
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@@ -444,7 +444,7 @@
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1ffb5f28>"
"<matplotlib.figure.Figure at 0x7f0879754198>"
]
}
],
@@ -476,9 +476,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1ffcce80>"
"<matplotlib.figure.Figure at 0x7f0879a3c048>"
]
}
],
@@ -576,9 +576,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fdb8c88>"
"<matplotlib.figure.Figure at 0x7f0879518240>"
]
}
],
@@ -619,9 +619,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fec8e48>"
"<matplotlib.figure.Figure at 0x7f08795384e0>"
]
}
],
@@ -659,9 +659,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1ff97a20>"
"<matplotlib.figure.Figure at 0x7f08753f8f60>"
]
}
],
@@ -700,9 +700,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd1c320>"
"<matplotlib.figure.Figure at 0x7f08796890f0>"
]
}
],
@@ -760,9 +760,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1ff8f2e8>"
"<matplotlib.figure.Figure at 0x7f08794aa780>"
]
}
],
@@ -813,7 +813,7 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fcbf7b8>"
"<matplotlib.figure.Figure at 0x7f0879403a20>"
]
}
],
@@ -867,9 +867,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd8b320>"
"<matplotlib.figure.Figure at 0x7f08793f9cc0>"
]
}
],
@@ -973,9 +973,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd8b2e8>"
"<matplotlib.figure.Figure at 0x7f087940ff98>"
]
}
],
@@ -1041,9 +1041,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb180714e0>"
"<matplotlib.figure.Figure at 0x7f087542a4e0>"
]
}
],
@@ -1159,9 +1159,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fbb1fcc3b38>"
"<matplotlib.figure.Figure at 0x7f08753438d0>"
]
}
],
@@ -1193,9 +1193,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd527f0>"
"<matplotlib.figure.Figure at 0x7f087532c710>"
]
}
],
@@ -1220,9 +1220,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fed4c88>"
"<matplotlib.figure.Figure at 0x7f08753f90f0>"
]
}
],
@@ -1248,17 +1248,17 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb17c31908>"
"<matplotlib.figure.Figure at 0x7f08719ec9e8>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd957b8>"
"<matplotlib.figure.Figure at 0x7f08719ec908>"
]
}
],
@@ -1289,41 +1289,41 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd5fb70>"
"<matplotlib.figure.Figure at 0x7f08794be278>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd5f8d0>"
"<matplotlib.figure.Figure at 0x7f0879475978>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb1fcc9048>"
"<matplotlib.figure.Figure at 0x7f0879443828>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fbb1fd42748>"
"<matplotlib.figure.Figure at 0x7f0879443a20>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fbb16384e80>"
"<matplotlib.figure.Figure at 0x7f087541c780>"
]
}
],
@@ -1471,9 +1471,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7fbb20de64e0>"
"<matplotlib.figure.Figure at 0x7f08797657b8>"
]
}
],
15_HInfinity_Filters.ipynb
+6 -5
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:3902df81d4a65468d74c91a7d5441eba58bde48d630e4949c25bd244969debdb"
"signature": "sha256:34c82e678d7fb9149196643d33a3ee1faa1ff35f46a5b1aeb05295814c524598"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -16,10 +16,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
" # $H_\\infty$ filter"
"$H_\\infty$ filter"
]
},
{
@@ -257,7 +258,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f781e4548d0>"
"<IPython.core.display.HTML at 0x7ff9807795c0>"
]
}
],
@@ -332,7 +333,7 @@
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7801fc2278>"
"<matplotlib.figure.Figure at 0x7ff960007dd8>"
]
}
],
16_Ensemble_Kalman_Filters.ipynb
+29 -21
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:f4b70cfb28ac903575af4e3ed759729739f9f71f6da9fd79075fdd442ab3a844"
"signature": "sha256:66b0644811e171f1049b6128b1a78a8f667ebb62ac0e53606151e249e2e3b53d"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -16,10 +16,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"#Ensemble Kalman Filters"
"Ensemble Kalman Filters"
]
},
{
@@ -258,7 +259,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7effb0335320>"
"<IPython.core.display.HTML at 0x7f116d5ea908>"
]
}
],
@@ -309,19 +310,20 @@
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x7eff933866d8>"
"<matplotlib.figure.Figure at 0x7f1151166748>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##The Algorithm"
"The Algorithm"
]
},
{
@@ -336,10 +338,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"###Initialize Step"
"Initialize Step"
]
},
{
@@ -355,10 +358,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"###Predict Step"
"Predict Step"
]
},
{
@@ -410,10 +414,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"###Update Step"
"Update Step"
]
},
{
@@ -513,10 +518,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Implementation and Example"
"Implementation and Example"
]
},
{
@@ -626,7 +632,7 @@
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7eff8c048518>"
"<matplotlib.figure.Figure at 0x7f114cdf6160>"
]
}
],
@@ -640,10 +646,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##Outstanding Questions"
"Outstanding Questions"
]
},
{
@@ -689,10 +696,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##References"
"References"
]
},
{
Appendix_A_Installation.ipynb
+20 -14
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:012b13d3e889e7a7b88e356eb4af5a1b862d7aad3511e4b9d1d606066d978bb6"
"signature": "sha256:540d1e6c56d55cb8bbd3b8c0602202957efaffef618afff22dc2c55095c22668"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -16,10 +16,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"#Installation, Python, Numpy, and filterpy"
"Installation, Python, Numpy, and filterpy"
]
},
{
@@ -258,7 +259,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f0785104438>"
"<IPython.core.display.HTML at 0x7f2a6a04f908>"
]
}
],
@@ -276,10 +277,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"## Installing the SciPy Stack"
"Installing the SciPy Stack"
]
},
{
@@ -298,10 +300,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"### Manual Install of the SciPy stack"
"Manual Install of the SciPy stack"
]
},
{
@@ -373,10 +376,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"## Installing/downloading and running the book"
"Installing/downloading and running the book"
]
},
{
@@ -398,10 +402,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"## Using IPython Notebook"
"Using IPython Notebook"
]
},
{
@@ -438,10 +443,11 @@
]
},
{
"cell_type": "markdown",
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"##SymPy"
"SymPy"
]
},
{