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

further small changes

Browse Source
This commit is contained in:
toni
2018-10-18 12:59:46 +02:00
parent e0c800bdbc
commit 96d7b92683
3 changed files with 24 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
tex_review/chapters/relatedwork.tex
View File

@@ -80,13 +80,14 @@ The estimated parameters can be refined using additional walks.
Within this work we present a similar optimization approach for estimating the AP's location in 3D.
However, instead of taking multiple measuring walks, the locations are optimized based only on some reference measurements, further decreasing the setup-time.
Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
\commentByToni{Die Quelle aus den Reviews. Wir können auch Kontinuierlich. Der hat das Problem das er entweder überall gewesen sein muss, oder interpolieren.}
%immpf
Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are based on the nature of \add{a} particle filter.
They are often caused by restrictive assumptions about the dynamic system, like seen from the aforementioned problem of sample impoverishment.
The authors of \cite{Sun2013} handled the problem by using an adaptive number of particles instead of a fixed one.
The key idea is to choose a small number of samples if the distribution is focused on a small part of the state space and a large number of particles if the distribution is much more spread out and requires a higher diversity of samples.
The problem of sample impoverishment is then addressed by adapting the number of particles dependent upon the system's current uncertainty \cite{Fetzer-17}.
The problem of sample impoverishment is then addressed by adapting the number of particles dependent upon the system's current uncertainty.
%\commentByFrank{ich glaube encountered ist das falsche wort. du willst doch auf 'es wird gefixed' raus, oder? addressed? mitigated?}
In practice, sample impoverishment is often a problem of environmental restrictions and system dynamics.
@@ -98,8 +99,8 @@ Thus, a much simpler, but heuristic method is presented within this paper.
%estimation
Finally, as the name recursive state estimation says, it requires to find the most probable state within the state space, to provide the "best estimate" of the underlying problem.
In the discrete manner of a particle representation this is often done by providing a single value, also known as sample statistic, to serve as a best guess \cite{Bullmann-18}.
Examples are the weighted-average over all particles or the particle with the highest weight.
In the discrete manner of a particle representation this is often done by providing a single value, also known as sample statistic, to serve as a best guess \cite{bar2004estimation}.
Examples are the weighted-average over all particles or the particle with the highest weight \cite{blanco2009phd}.
However, in complex scenarios like a multimodal representation of the posterior, such methods fail to provide an accurate statement about the most probable state.
Thus, in \cite{Bullmann-18} we present a \del{rapid computation} \add{approximation} scheme of kernel density estimates (KDE).
Recovering the probability density function using an efficient KDE algorithm yields a promising approach to solve the state estimation problem in a more profound way.