%0 Journal Article
%T WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter
%A Yingjie Sun
%A Yi Zhong
%A Congwei Hu
%A Ao Xiong
%A Hu Zhao
%J Open Journal of Applied Sciences
%P 379-390
%@ 2165-3925
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojapps.2024.142026
%X Aiming at the problem that the positioning accuracy of WiFi indoor
positioning technology based on location fingerprint has not reached the
requirements of practical application, a WiFi indoor positioning and tracking
algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS)
and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to
generate clustering fingerprints with optimal clustering effect performance; In
the online phase, CS and nearest neighbor algorithm are used for position
estimation; Finally, the Kalman filter and physical constraints are combined to
perform positioning and tracking. By collecting a large number of real
experimental data, it is proved that the developed algorithm has higher
positioning accuracy and more accurate trajectory tracking effect.
%K WiFi Indoor Positioning
%K Cluster
%K Signal Recovery
%K Trajectory Tracking
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131338