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{
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{
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"cells": [
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"cell_type": "markdown",
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"source": [
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"### Design the Measurement Noise Matrix\n",
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"\n",
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"Wee assume that the $x$ and $y$ variables are independent white Gaussian processes. That is, the noise in x is not in any way dependent on the noise in y, and the noise is normally distributed about the mean 0. For now let's set the variance for $x$ and $y$ to be 5 meters$^2$. They are independent, so there is no covariance, and our off diagonals will be 0. This gives us:\n",
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"We assume that the $x$ and $y$ variables are independent white Gaussian processes. That is, the noise in x is not in any way dependent on the noise in y, and the noise is normally distributed about the mean 0. For now let's set the variance for $x$ and $y$ to be 5 meters$^2$. They are independent, so there is no covariance, and our off diagonals will be 0. This gives us:\n",
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"\n",
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"$$\\mathbf R = \\begin{bmatrix}\\sigma_x^2 & \\sigma_y\\sigma_x \\\\ \\sigma_x\\sigma_y & \\sigma_{y}^2\\end{bmatrix} \n",
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"= \\begin{bmatrix}5&0\\\\0&5\\end{bmatrix}$$\n",
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"\n",
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"This is also called a *constant velocity* model, because of the assumption of a constant velocity.\n",
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"\n",
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"A second order has a second derivative. The second derivative of position is acceleration, with the equation\n",
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"A second order system has a second derivative. The second derivative of position is acceleration, with the equation\n",
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"\n",
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"$$a = \\frac{d^2x}{dt^2}$$\n",
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"\n",
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