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added conclusion kleiner fix nummer 2

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Toni
2016-02-14 18:03:14 +01:00
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tex/chapters/conclusion.tex
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@@ -4,9 +4,14 @@ Based on a weighted graph, two different models for walking in a more targeted a
It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexible for uncertain behaviour. 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. Furthermore, our approach is able to provide accurate and robust position estimations, even when (normally) necessary calibration processes are ignored.
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. 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.
As seen, multimodal distributions lead to faulty position estimations and therefore a rising error. One possible method to resolve this issue would be a more suiting location estimation method. 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. As seen, multimodal distributions lead to faulty position estimations and therefore a rising error.
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.