27 lines
2.0 KiB
TeX
27 lines
2.0 KiB
TeX
\section{Conclusion}
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We presented a novel approach to integrate prior navigation knowledge by using realistic human walking paths.
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Based on a weighted graph, two different models for walking in a targeted and natural manner were introduced.
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It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexibility for unexpected behaviour.
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Furthermore, our approach is able to provide accurate and robust position estimations, even when (usually) necessary calibration processes are omitted.
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However, providing this calibration knowledge can further improve the results.
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In order to reduce the effort of locating and calibrating \docAP{}s, a numerical optimization based on
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measurements during predefined walks could be considered.
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Additionally, the graph allows for storing pre-computed signal strengths and thus enables more complex
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prediction models e.g. incorporating wall information.
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As seen, multimodal distributions lead to faulty position estimations and therefore rising errors.
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One possible method to resolve this issue would be a more suiting location estimation technique.
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Another promising way is smoothing.
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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.
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%\commentByFrank{balance zwischen den einzelnen wahrscheinlichkeiten ist oft ein schmaler grad. wieviel turn erlauben, wieviel auf den pfad zwingen. das verbesern}
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%\commentByFrank{position der APs wissen ist viel arbeit. vereinfachen durch test-walks auf vorgegebenen pfaden -> numerisch optimieren wo APs sind}
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%\commentByToni{quadtress. stellen die groesse der zellen variable ein. je nach bedarf.}
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%\commentByFrank{multimodalitaeten (z.B. treppenhaeuser). fixen durch andere estimations}
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%\commentByToni{oder durch smoothing}
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%\commentByToni{Aufzuege hinzufuegen. Vertical Acceleration benutzen.}
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