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

Merge pull request #123 from ASunday47/patch-2

Browse Source 
Update 07-Kalman-Filter-Math.ipynb
This commit is contained in:
Roger Labbe 2016-09-23 12:49:25 -07:00 committed by GitHub
commit 0b358aca26
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
07-Kalman-Filter-Math.ipynb
View File

@ -754,7 +754,7 @@
"source": [
"## Design of the Process Noise Matrix\n",
"\n",
"In general the design of the $\\mathbf Q$ matrix is among the most difficult aspects of Kalman filter design. This is due to several factors. First, the math requires a good foundation in signal theory. Second, we are trying to model the noise in something for which we have little information. Consider trying to model the process noise for a thrown baseball. We can model it as a sphere moving through the air, but that leave many unknown factors - the wind, ball rotation and spin decay, the coefficient of drag of a ball with stitches, the effects of wind and air density, and so on. We develop the equations for an exact mathematical solution for a given process model, but since the process model is incomplete the result for $\\mathbf Q$ will also be incomplete. This has a lot of ramifications for the behavior of the Kalman filter. If $\\mathbf Q$ is too small then the filter will be overconfident in its prediction model and will diverge from the actual solution. If $\\mathbf Q$ is too large than the filter will be unduly influenced by the noise in the measurements and perform sub-optimally. In practice we spend a lot of time running simulations and evaluating collected data to try to select an appropriate value for $\\mathbf Q$. But let's start by looking at the math.\n",
"In general the design of the $\\mathbf Q$ matrix is among the most difficult aspects of Kalman filter design. This is due to several factors. First, the math requires a good foundation in signal theory. Second, we are trying to model the noise in something for which we have little information. Consider trying to model the process noise for a thrown baseball. We can model it as a sphere moving through the air, but that leaves many unknown factors - the wind, ball rotation and spin decay, the coefficient of drag of a ball with stitches, the effects of wind and air density, and so on. We develop the equations for an exact mathematical solution for a given process model, but since the process model is incomplete the result for $\\mathbf Q$ will also be incomplete. This has a lot of ramifications for the behavior of the Kalman filter. If $\\mathbf Q$ is too small then the filter will be overconfident in its prediction model and will diverge from the actual solution. If $\\mathbf Q$ is too large than the filter will be unduly influenced by the noise in the measurements and perform sub-optimally. In practice we spend a lot of time running simulations and evaluating collected data to try to select an appropriate value for $\\mathbf Q$. But let's start by looking at the math.\n",
"\n",
"\n",
"Let's assume a kinematic system - some system that can be modeled using Newton's equations of motion. We can make a few different assumptions about this process. \n",