From e39295caf5ca8f1fa3846c97f2439a0028f4594a Mon Sep 17 00:00:00 2001 From: sleepingAgain Date: Tue, 20 Jun 2017 13:09:40 +0100 Subject: [PATCH] Fixed typo in Chapter 7 Changed stoochastic to stochastic. --- 07-Kalman-Filter-Math.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/07-Kalman-Filter-Math.ipynb b/07-Kalman-Filter-Math.ipynb index d5ebc68..824f229 100644 --- a/07-Kalman-Filter-Math.ipynb +++ b/07-Kalman-Filter-Math.ipynb @@ -795,7 +795,7 @@ "\n", "$$\\mathbf{Q_c} = \\begin{bmatrix}0&0&0\\\\0&0&0\\\\0&0&1\\end{bmatrix} \\Phi_s$$\n", "\n", - "where $\\Phi_s$ is the spectral density of the white noise. This can be derived, but is beyond the scope of this book. See any standard text on stoochastic processes for the details. In practice we often do not know the spectral density of the noise, and so this turns into an \"engineering\" factor - a number we experimentally tune until our filter performs as we expect. You can see that the matrix that $\\Phi_s$ is multiplied by effectively assigns the power spectral density to the acceleration term. This makes sense; we assume that the system has constant acceleration except for the variations caused by noise. The noise alters the acceleration.\n", + "where $\\Phi_s$ is the spectral density of the white noise. This can be derived, but is beyond the scope of this book. See any standard text on stochastic processes for the details. In practice we often do not know the spectral density of the noise, and so this turns into an \"engineering\" factor - a number we experimentally tune until our filter performs as we expect. You can see that the matrix that $\\Phi_s$ is multiplied by effectively assigns the power spectral density to the acceleration term. This makes sense; we assume that the system has constant acceleration except for the variations caused by noise. The noise alters the acceleration.\n", "\n", "We could carry out these computations ourselves, but I prefer using SymPy to solve the equation.\n", "\n",