toni/IPIN2016
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

some minor changes again

Browse Source
This commit is contained in:
toni
2016-04-29 18:22:12 +02:00
parent 9cb091d707
commit 3f6107f2a1
2 changed files with 2 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
tex/chapters/smoothing.tex
View File

@@ -85,9 +85,8 @@ Unlike the transition presented in section \ref{sec:transition}, it is not possi
Here, $p(\vec{q}_{t+1} \mid \vec{q}_{t})$ needs to provide the probability of the \textit{known} future state $\vec{q}_{t+1}$ under the condition of the current state $\vec{q}_{t}$.
In case of indoor localisation using particle filtering, it is necessary to not only provide the probability of moving to a particle's position under the condition of its ancestor, but also of all other particles at time $t$.
The smoothing transition model therefore calculates the probability of being in a state $\vec{q}_{t+1}$ in regard to previous states and the pedestrian's walking behaviour.
This means that a state $\vec{q}_t$ gets rewarded with a high probability, if it is a proper ancestor (realistic previous position) of a future state $\vec{q}_{t+1}$.
%observations von barometer und turn sind ziemlich genau.
%of course, instead of the line of side one could choose to calculate the the shortest path. however, this requires a trombendes calculation time and is therefore not further discussed within this work.
This means that a state $\vec{q}_t$ is more likely if it is a proper ancestor (realistic previous position) of a future state $\vec{q}_{t+1}$.
In the following a simple and inexpensive approach for receiving this information will be described.
By writing
\begin{equation}