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related work angefangen.. aufbau aber noch doof

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Toni
2016-04-15 17:08:34 +02:00
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tex/chapters/introduction.tex
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@@ -16,9 +16,9 @@ In most cases, probabilistic methods are used to incorporate those highly differ
Here, a probability distribution describes the pedestrian's possible whereabouts and therefore the uncertainty of the system.
Drawing from a probability distribution and finding an analytical solution for densities is in most cases a difficult task, especially in case of time sequential, non-linear and non-Gaussian models.
Due to the high complexity of the human movement, we consider indoor localisation as such.
A broad class to obtain numerical results instead are the Monte Carlo methods.
A broad class to obtain numerical results instead are the Monte Carlo (MC) methods.
Here, a set of weighted random samples is used to solve any problem having a probabilistic interpretation.
By applying the time sequential hidden Markov process of Bayes filtering, one of the most important Monte Carlo techniques results: particle filtering.
By applying the time sequential hidden Markov process of Bayes filtering, one of the most important MC techniques results: particle filtering.
A particle filter updates the state estimation recursively in time with every new incoming measurement using the state transition and state evaluation step.
Based on this general methodology, many different approaches for estimating a position in indoor environments have been developed.
@@ -64,7 +64,7 @@ However, standard filtering methods are not able to use any future information a
One very promising way to deal with these problems is smoothing.
Smoothing methods are able to make use of future measurements for computing its estimation.
By running backwards in time, they are also able to remove multimodalities and improve the overall localization result.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of Monte Carlo methods.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of MC methods.
%Of course, this excludes linear procedures like Kalman filtering.
Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsill04:MCS}.