Xin Li

TL;DR: Two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure are proposed to improve the positioning accuracy and the elimination of various phenomena.

TL;DR: A Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem.

TL;DR: The “cross-wall” problem is solved based on the development of a floor-map-aided particle filter algorithm by weighting the particles, thereby also eliminating the gross-error effects originating from WiFi or P-O measurements.

TL;DR: An adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering that outperforms traditional fingerprinting methods for estimating user’s position during online stage.

TL;DR: The performance of Incremental Smoothing is compared with state of the art fusion algorithms based on EKF, and it is shown that the incremental smoothing algorithm can achieve real-time positioning while exhibiting stronger robustness against intermittent noise, continuous noise and continuous interruption abnormality of UWB data.