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sample impoverishment weiter gemacht

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toni
2018-03-28 17:12:37 +02:00
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@@ -11,7 +11,7 @@ Here, new particles are drawn according to some importance distribution, often r
Those particles are then weighted by the state evaluation given different sensor measurements.
A resampling step is deployed to prevent that only a small number of particles have a signifcant weight \cite{chen2003bayesian}.
Most localisation approaches differ mainly in how the transition and evaluation steps are implemented and the available sensors are incorporated \cite{Fetzer-16, Ebner-16, Hilsenbeck2014}.
Additionally, within this paper we present a method, which is designed to run solely on a smartphone.
Additionally, within this paper we present a method, which is designed to run solely on a commercial smartphone.
In its most basic form, the state transition is given by.. einfach distanz und heading.. intersection with walls usw.
@@ -48,13 +48,25 @@ Thus \cite{Ebner-17} suggests to only consider floors/ceilings, what can be calc
To further reduce the setup-time, \cite{WithoutThePain} introduces an approach that works without any prior knowledge.
They use a genetic optimization algorithm to estimate the parameters for a signal strength prediction, including the access points (AP) position, and the pedestrian's locations during the walk.
The estimated parameters can be refined using additional walks.
Within this work we present a similar optimization approach for estimating the AP's location.
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, what further decreases the setup-time.
Additionally, our approach extends to the third dimension.
Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
%immpf
Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are based on the nature of particle filter.
They are often caused by restrictive assumptions about the dynamic system, like the aforementioned 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 encountered by adapting the number of particles depend upon the systems current uncertainty \cite{Fetzer-17}.
Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are .. based on the nature of particle filters. One very affecting problem is the before mentioned sample impoverishment. In blabal \cite{} this problems was tackled by and. In \cite{} we deployed a ... . However, deploying a IMMPF is in most cased not a necassary step, thus we present i much simple, but also very heuristic model within this paper.
However, in practice sample impoverishment is often a problem of environmental restrictions and system dynamics.
Therefore, such a method fails, since it is not able to propagate new particles into the state space due to environmental restrictions e.g. walls or ceilings.
In \cite{Fetzer-17} we deployed an interacting multiple model particle filter (IMMPF) to solve the sample impoverishment.
We combine two particle filter using a non-trivial Markov switching process, depending upon the Kullback-Leibler divergence between both.
However, deploying a IMMPF is in most cased not a necessary step, thus we present i much simple, but also very heuristic model within this paper.
%estimation
Finally, as the name recursive state estimation states, 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 sample representation this is often done by providing a single value, also known as sample statistic, to serve as a “best guess”.
This value is then calculated by means of simple parametric point estimators, e.g. the weighted-average over all samples, the sample with the highest weight or by assuming other parametric statistics like normal distributions