From 6587cae808f6fa07781c2aced482bbe2d15e627b Mon Sep 17 00:00:00 2001 From: Toni Date: Sun, 14 Feb 2016 18:00:12 +0100 Subject: [PATCH] added conclusion kleiner fix --- tex/chapters/conclusion.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tex/chapters/conclusion.tex b/tex/chapters/conclusion.tex index 9d91c02..25de8c9 100644 --- a/tex/chapters/conclusion.tex +++ b/tex/chapters/conclusion.tex @@ -6,7 +6,7 @@ Furthermore, our approach is able to provide accurate and robust position estima 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 estimate 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 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.