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first draft introduction

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Toni
2016-04-14 17:30:15 +02:00
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tex/chapters/introduction.tex
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@@ -23,7 +23,7 @@ A particle filter updates the state estimation recursively in time with every ne
Based on this general methodology, many different approaches for estimating a position in indoor environments have been developed.
All these approaches differ mainly in how the dynamics are modelled in the transition step and how a specific sensor measurement can be used for evaluation.
For example, recent approaches are using a graph-based structure to consider environmental restrictions (walking through walls) and the characteristics of human movement (walking speed) within the transition model \cite{Ebner-15}.
For example, recent approaches are using a graph-based structure to consider environmental restrictions (walking through walls) and the characteristics of human movement (walking speed) within the transition model \cite{Ebner-15, Nurminen2014, Hilsenbeck2014}.
The evaluation model is mostly separated into any number of sensor models, each representing the probability for a noisy measurement in regard to the current position.
For example, a barometer can be used to determine the probability of being on a certain floor \cite{Binghao13-UBI}.
%Another example that demonstrates the big differences between single approaches is the large number of sensor models using Wi-Fi signal strengths. There are fingerprinting methods, which require an extensive offline calibration phase, signal strength prediction models like the log-distance model or wall-attenuation-factor model and many others \cite{Ville09, Fang09, Ebner:Thesis:2013}.
@@ -35,10 +35,10 @@ That is the reason for the use of statistical methods in the first place. Nevert
Current transition models, which aim to approximate the movement, are still very restrictive and unable to handle unforeseen events.
Faulty sensor measurements, like a falsely detected turn, can cause the estimation to lose track.
For example by taking a turn too soon and walking into a room instead of another big hallway \cite{Ebner-15}.
For example by taking a turn too soon and walking into a room instead of another big hallway.
Due to this, the filter needs some time to recover, which again takes a while because of the restrictive model (e.g. no walking through walls and only realistic walking speed).
This temporal delay worsens the estimate immensely.
A solution to recover from such filter divergences faster, is using methods for re-initializing the filtering procedure \cite{Nurminen14-MMF}.
A solution to recover from such filter divergences faster, is using methods for re-initializing the filtering procedure \cite{Nurminen2014}.
However, even this can not completely prevent delays.
Another reason for possible time delays are slow sensor updates.
For example, most mobile devices restrict the Wi-Fi module to update only every few seconds, to save on battery.
@@ -61,8 +61,19 @@ As one can imagine, this can lead to serious problems in big indoor environments
Such a situation can be improved by incorporating future measurements (e.g. the right turn) or predictive information (e.g. the most likely path) to the filtering procedure \cite{Ebner-16}.
However, standard filtering methods are not able to use any future information and the possibilities to make a distant forecast are also limited \cite{robotics, Doucet11:ATO, chen2003bayesian}.
One very promising way to deal with these problems is smoothing.
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.
%Of course, this excludes linear procedures like Kalman filtering.
Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsill04:MCS}.
Within this work, we investigate the benefits and drawbacks of those techniques using our indoor localisation system presented in \cite{Ebner-16}.
We provide both, fixed-lag and fixed-interval smoothing as well as two novel approaches for incorporating them easily within the localisation procedure.
The main goal is to solve the above mentioned problems and to investigate new possibilities for even more advanced systems.
Within this work, we try to address those problems