From 6f588d39cb902677a939ada89b5ad64f016742e7 Mon Sep 17 00:00:00 2001 From: toni Date: Mon, 9 Jul 2018 21:46:03 +0200 Subject: [PATCH] mit text optimization angefangen. aber nerv am ruecken eingeklemmt. aua. --- tex/chapters/experiments.tex | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tex/chapters/experiments.tex b/tex/chapters/experiments.tex index 008f55c..d3603df 100644 --- a/tex/chapters/experiments.tex +++ b/tex/chapters/experiments.tex @@ -42,6 +42,16 @@ Within all Wi-Fi observations, we only consider the beacons, which are identifie Other transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors. Fig. compares optimized ap vs real positions for the ground level, thus we only illustrated optimized ap', which are really installed there. red created using the global optimization scheme, blue a optimized only for the rechteckigen ground floor. + +of course, the position alone does not provide sufficient information of the overall perfomance of the optimiziation since they give no information about the other optimized parameters (bla, blub and bliib). +Nevertheless, fig. \ref{} gives an idea on how optimizing a simplified signal-strength prediction model behaves. +By only considering ceillings, the attenuation factore... and thus most z coordinates for the ap's are similiar. +The main message from this is, that wherever the ap's are optimized they are optimized to perfectly fit the underlying signal-strength model. +thus the optimized parameters provide far better localization results compared to just using the real ones. simply because modelling the realistic incidents is very time consuming. + +difference stockwerk, global +looking at the optimziation errors, this can be varified... etc pp + %wie fingerprints aufgenommen, wie viele ... \todo{Vom Journal Paper 2017 noch diese rote optimierungsgrafik. fig 5. Das wäre eigentlich auch echt nicht schlecht. und dazu auch die werte "results from the