FHWS/IPIN2018
Archived
4
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Changes

Browse Source
This commit is contained in:
MBulli
2018-10-20 18:51:07 +02:00
parent 837963b4e8
commit 5cfe410869
6 changed files with 58 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tex_review/chapters/estimation.tex
View File

@@ -15,7 +15,7 @@
% Problems: larger error compared to WA and bandwidth selection
Each particle is a realization of one possible system state, here, the position of a pedestrian within a building.
Each particle is a realization of one possible system state, here the position of a pedestrian within a building.
The set of all particles represents the posterior of the system.
In other words, the particle filter naturally generates a sample based representation of the posterior.
With this representation a point estimator can directly be applied to the sample data to derive a sample statistic serving as a \qq{best guess}.
@@ -30,7 +30,7 @@ In the case of particle filters the MMSE estimate equals to the weighted-average
where $W_t=\sum_{i=1}^{N}w^i_t$ is the sum of all weights.
While producing an overall good result in many situations, it fails when the posterior is multimodal.
In these situations the weighted-average estimate will find the estimate somewhere between the modes.
\del{Clearly}\add{It is expected that}, such a position between modes is extremely unlikely the position of the pedestrian.
\del{Clearly}\add{It is expected that} such a position between modes is extremely unlikely the position of the pedestrian.
The real position is more likely to be found at the position of one of the modes, but virtually never somewhere between.
In the case of a multimodal posterior the system should estimate the position based on the highest mode.