From 394f0a2570111558e0b684df73c34d42ad864605 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Mon, 2 Jun 2014 12:03:25 -0700 Subject: [PATCH] Got basic tracking working with 'magic numbers' for initial pos,vel,acc. --- Designing_Kalman_Filters.ipynb | 255 +++++++++++++++++++++++++++++---- 1 file changed, 224 insertions(+), 31 deletions(-) diff --git a/Designing_Kalman_Filters.ipynb b/Designing_Kalman_Filters.ipynb index 623e026..647359d 100644 --- a/Designing_Kalman_Filters.ipynb +++ b/Designing_Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:b1bffff1d77cb5172425b64338a0fc28e55b78e370a8acbcb65eb0549066ad29" + "signature": "sha256:9253d1b89f39ca3b6e9087392e93410a4b889d4de3c48de04660d0c028948c29" }, "nbformat": 3, "nbformat_minor": 0, @@ -1378,53 +1378,70 @@ "cell_type": "code", "collapsed": false, "input": [ - "f1 = KalmanFilter(dim=5)\n", - "dt = 0.1\n", - "g = 9.8\n", + "def ball_filter(dt):\n", + " f1 = KalmanFilter(dim=5)\n", + " g = 9.8\n", "\n", - "f1.F = np.mat ([[1, dt, 0, 0, 0],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 0, 1, dt, dt*dt*g],\n", - " [0, 0, 0, 1, dt],\n", - " [0, 0, 0, 0, 1]])\n", + " f1.F = np.mat ([[1., dt, 0, 0, 0],\n", + " [0, 1., 0, 0, 0],\n", + " [0, 0, 1., dt, -dt*dt*g],\n", + " [0, 0, 0, 1., dt],\n", + " [0, 0, 0, 0, 1.]])\n", "\n", - "f1.H = np.mat([[1,0,0,0,0],\n", - " [0,0,0,0,0],\n", - " [1,0,1,0,0],\n", - " [0,0,0,0,0],\n", - " [0,0,0,0,0]])\n", - "f1.B = 0.\n", + " f1.H = np.mat([[1,0,0,0,0],\n", + " [0,0,0,0,0],\n", + " [0,0,1,0,0],\n", + " [0,0,0,0,0],\n", + " [0,0,0,0,0]])\n", "\n", - "f1.R = np.eye(5) * 1.\n", - "f1.Q = np.zeros((5,5))\n", - "f1.Q[4,4] = 0.1\n", - "f1.x = np.mat([0,0,0,0,0]).T\n", - "f1.P = np.eye(5) * 5.\n", - "f1.u = np.mat([0,0,0,0,0]).T\n", - "\n" + "\n", + " f1.R = np.eye(5) * 1.\n", + " f1.Q = np.zeros((5,5))\n", + " f1.Q[4,4] = 0.1\n", + " f1.x = np.mat([0, 21.2 , 0, 20.7, -9.8]).T\n", + " f1.P = np.eye(5) * 5.\n", + " f1.B = 0.\n", + " f1.u = 0\n", + " \n", + " return f1" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 73 + "prompt_number": 170 }, { "cell_type": "code", "collapsed": false, "input": [ + "dt = 0.1\n", + "f1 = ball_filter(dt)\n", "# initialize storage and other variables for the run\n", "count = 30\n", "xs, ys = [],[]\n", "pxs, pys = [],[]\n", "\n", - "traj = BallTrajectory2D (30., angle_degrees=45.)\n", - "\n", - "for t in np.arange(0,0.4,dt):\n", + "traj = BallTrajectory2D (30., angle_degrees=45., noise_scale=0.3)\n", + "traj.update(0)\n", + "x,y= (traj.x,traj.y)\n", + "traj.update(dt)\n", + "print (f1.x)\n", + "print (traj.x - x, traj.y - y)\n", + "log = True\n", + "#print (f1.F*f1.x)\n", + "for t in np.arange(dt,5,dt):\n", " traj.update(t)\n", " z = np.mat([traj.x, 0, traj.y, 0,0]).T\n", + " if log: print (traj.x, traj.y)\n", "\n", " f1.predict ()\n", + " if log: print (f1.x.T)\n", + " \n", + " #f1.x[4]=-9.8\n", + " \n", " f1.update (z)\n", + " if log: print (f1.x.T)\n", + "\n", "\n", " xs.append (f1.x[0,0])\n", " ys.append (f1.x[2,0])\n", @@ -1439,16 +1456,189 @@ "language": "python", "metadata": {}, "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 0. ]\n", + " [ 21.2]\n", + " [ 0. ]\n", + " [ 20.7]\n", + " [ -9.8]]\n", + "2.26334154798 2.2701914807\n", + "2.7486356737 1.99227097822\n", + "[[ 2.12 21.2 3.0304 19.72 -9.8 ]]\n", + "[[ 2.64472895 21.25195336 2.16251132 19.6432216 -9.71658223]]\n", + "4.00471898834 3.77548778907\n", + "[[ 4.76992429 21.25195336 5.07905854 18.67156338 -9.71658223]]\n", + "[[ 4.40728351 21.01906479 4.44234009 18.35760646 -9.36803761]]\n", + "6.67939142944 5.71113377806\n", + "[[ 6.50918999 21.01906479 7.19616842 17.4208027 -9.36803761]]\n", + "[[ 6.57184657 21.10323034 6.60307241 16.91399233 -8.79487404]]\n", + "8.7006186025 7.65399749306\n", + "[[ 8.6821696 21.10323034 9.1563693 16.03450493 -8.79487404]]\n", + "[[ 8.68840872 21.11463517 8.5903118 15.46920598 -8.16329089]]\n", + "10.7741338917 9.0855524088\n", + 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utWRny/XKK5FeSa9i4wwAAIBua/nqVzWgk/fmiGbMOAMAAKDbYg4ckPuJJ3T8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\nxYsX6ytf+cpp98vGGQAAAKd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xbVwGl+IkIqvBBplqxdSzWLKcHNhPmACHCRNQ8tZb0I4efdfHx10rwLL9SZjf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\nv7CLZS66Mxv9mYvuutfI9ASl94nVuv3Hu+X69Be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"text": [ - "" + "" ] } ], - "prompt_number": 76 + "prompt_number": 177 }, { "cell_type": "code", @@ -1462,10 +1652,13 @@ { "cell_type": "code", "collapsed": false, - "input": [], + "input": [ + "%run?" + ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "prompt_number": 166 }, { "cell_type": "code",