Fixed estimates that were not tracking under 'Control Inputs'. Measurements were generated under assumption of dt=1, but were updating states from model using dt=0.1.

08-Designing-Kalman-Filters.ipynb

@@ -339,7 +339,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -456,7 +458,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from filterpy.kalman import KalmanFilter\n",
@@ -583,7 +587,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "tracker.H = np.array([[1/0.3048, 0, 0,        0],\n",
@@ -649,7 +655,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "tracker.x = np.array([[0, 0, 0, 0]]).T\n",
@@ -980,7 +988,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def ZeroOrderKF(R, Q, P=20):\n",
@@ -1042,7 +1052,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def FirstOrderKF(R, Q, dt):\n",
@@ -1106,7 +1118,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def SecondOrderKF(R_std, Q, dt, P=100):\n",
@@ -1148,7 +1162,9 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def simulate_system(Q, count):\n",
@@ -1172,7 +1188,9 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def filter_data(kf, zs):\n",
@@ -1231,7 +1249,9 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def plot_residuals(xs, filter_xs, Ps, title, y_label, stds=1):\n",
@@ -2110,9 +2130,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAEmCAYAAAAA8LatAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAEfCAYAAADMR4v2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x162c8cd2240>"
+       "<matplotlib.figure.Figure at 0x112e73ac8>"
       ]
      },
      "metadata": {},
@@ -2120,7 +2140,7 @@
     }
    ],
    "source": [
-    "dt = 0.1\n",
+    "dt = 1.\n",
     "R = 3.\n",
     "kf = KalmanFilter(dim_x=2, dim_z=1, dim_u = 1)\n",
     "kf.P *= 10\n",
@@ -2935,7 +2955,9 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from math import radians, sin, cos\n",
@@ -3228,7 +3250,9 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from math import sin,cos,radians\n",
@@ -3463,7 +3487,9 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from math import radians, sin, cos, sqrt, exp\n",
@@ -3739,7 +3765,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.5.2"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
   }
  },
  "nbformat": 4,