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latest TeX: grid, experiments, conclusion. some gfx changed

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FrankE
2016-02-14 18:35:33 +01:00
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tex/chapters/conclusion.tex
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\section{Conclusion}
We presented a novel approach for integrating prior navigation knowledge by using realistic human walking paths.
Based on a weighted graph, two different models for walking in a more targeted and natural manner were introduced.
It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexible for uncertain behaviour.
Furthermore, our approach is able to provide accurate and robust position estimations, even when (normally) necessary calibration processes are ignored.
It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexibility for unexpected behaviour.
Furthermore, our approach is able to provide accurate and robust position estimations, even when (usually) necessary calibration processes are omitted.
However, providing this calibration knowledge can further improve the results.
In order to reduce the effort of locating the \docAP{}s and calibrating them, a numerical optimization based on predefined walks could be considered.
Additionally, the graph allows for storing pre-computed signal strengths and thus enables more complex prediction models incorporating floor and wall information into the signal strength estimation.
In order to reduce the effort of locating and calibrating \docAP{}s, a numerical optimization based on
measurements during predefined walks could be considered.
Additionally, the graph allows for storing pre-computed signal strengths and thus enables more complex
prediction models e.g. incorporating wall information.
As seen, multimodal distributions lead to faulty position estimations and therefore a rising error.
As seen, multimodal distributions lead to faulty position estimations and therefore rising errors.
One possible method to resolve this issue would be a more suiting location estimation technique.
Another promising way is smoothing.
By deploying a fixed-lag smoother the system would still be perceived as real-time application, but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.
By deploying a fixed-lag smoother the system would still be perceived as real-time application,
but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.