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In recent research, indoor localisation systems are often based upon a recursive state estimation using particle filtering. Within this context, sample impoverishment is a crucial problem causing the position estimation to lose track or get stuck within a demarcated area. The sample impoverishment problem can therefore be described as a too small particle diversity, unable to sample enough particles...
Indoor localisation continues to be a topic of growing importance. Despite the advances made, several profound problems are still present. For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements. Within this work, we solve such problems with the help of Monte Carlo smoothing methods, namely forward-backward smoother and backward...
Navigating to a desired destination is a key aspect of indoor localisation. Up to this point many different systems using present or past information for estimating the pedestrian's position were presented. Our work proposes a novel approach that incorporates prior navigation knowledge by using realistic human walking paths. In order to create such paths, we present a method that assigns an importance-factor...
We present an indoor localisation system that integrates different sensor modalities, namely Wi-Fi, barometer, iBeacons, step-detection and turn-detection for localisation of pedestrians within buildings over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon random walks on graphs. This model also frees...
Indoor positioning systems are required for many new applications and, ideally, should provide high accuracy at zero costs for initial setup, maintenance and per user. Many approaches thus use an existing Wi-Fi infrastructure and smart-phones for pedestrian location estimation. While the well-known Wi-Fi fingerprinting provides good localization accuracy down to one meter, necessary time and costs...
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