daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix of latex.

Browse Source
This commit is contained in:
Roger Labbe 2015-07-30 21:46:24 -07:00
parent 625c89bee4
commit df2b92db79
1 changed files with 2 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
06-Multivariate-Kalman-Filters.ipynb
View File

@ -1057,7 +1057,7 @@
"\n",
"Both the measurement $\\mathbf{z}$ and state $\\mathbf{x}$ are vectors so we need to use a matrix to perform the conversion. The Kalman filter equation that performs this step is:\n",
"\n",
"$$\\textbf{y} = \\mathbf{z} - \\mathbf{H \\bar{x}}$$\n",
"$$\\mathbf{y} = \\mathbf{z} - \\mathbf{H \\bar{x}}$$\n",
"\n",
"where $\\textbf{y}$ is the residual, $\\mathbf{x^-}$ is the prior, $\\textbf{z}$ is the measurement, and $\\textbf{H}$ is the measurement function. So we take the prior, convert it to a measurement, and subtract it from the measurement our sensor gave us. This gives us the difference between our prediction and measurement in measurement space!\n",
"\n",
@ -1692,7 +1692,6 @@
"\n",
"I want you to compare the equation for the system uncertainty and the covariance\n",
"\n",
"\n",
"$$\\begin{aligned}\n",
"\\mathbf{S} &= \\mathbf{H\\bar{P}H}^\\mathsf{T} + \\mathbf{R}\\\\\n",
"\\mathbf{\\bar{P}} &= \\mathbf{FPF}^\\mathsf{T} + \\mathbf{Q}\n",
@ -2893,7 +2892,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
"version": "3.4.1"
}
},
"nbformat": 4,