daviddoji/Kalman-and-Bayesian-Filters-in-Python
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typo fixes

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Roger Labbe 2015-07-12 16:16:08 -07:00
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Interactions.ipynb
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@ -36,11 +36,11 @@
"# Experimenting with FPF'\n",
"\n",
"\n",
"The Kaman filter uses the equation $P^- = FPF^\\mathsf{T}$ to compute the prior of the covariance matrix during the prediction step, where P is the covariannce matrix and F is the system transistion function. For a Newtonian system $x = \\dot{x}\\Delta t + x_0$ F might look like\n",
"The Kalman filter uses the equation $P^- = FPF^\\mathsf{T}$ to compute the prior of the covariance matrix during the prediction step, where P is the covariance matrix and F is the system transistion function. For a Newtonian system $x = \\dot{x}\\Delta t + x_0$ F might look like\n",
"\n",
"$$F = \\begin{bmatrix}1 & \\Delta t\\\\0 1\\end{bmatrix}$$\n",
"\n",
"$FPF^\\mathsf{T}$ alters P by taking the correlation between the position ($x$) and velocity ($\\dot{x}). This interactive plot lets you see the effect of different designs of F has on this value. For example,\n",
"$FPF^\\mathsf{T}$ alters P by taking the correlation between the position ($x$) and velocity ($\\dot{x}$). This interactive plot lets you see the effect of different designs of F has on this value. For example,\n",
"\n",
"* what if $x$ is not correlated to $\\dot{x}$? (set F01 to 0)\n",
"\n",