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tex/chapters/abstract.tex
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Indoor localisation continuous to be a topic of growing importance.
Despite the advances made, several profound problems are still present.
For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements.
Within this work, we try to solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation.
Within this work, we solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation.
In contrast to normal filtering procedures like particle filtering, smoothing methods are able to incorporate future measurements instead of just using current and past data.
This enables many possibilities for further improving the position estimation.
Both smoothing techniques are deployed as fixed-lag and fixed-interval smoother and a novel approach for incorporating them easily within our localisation system is presented.