TITLE:
WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter
AUTHORS:
Yingjie Sun, Yi Zhong, Congwei Hu, Ao Xiong, Hu Zhao
KEYWORDS:
WiFi Indoor Positioning, Cluster, Signal Recovery, Trajectory Tracking
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.14 No.2,
February
27,
2024
ABSTRACT: 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.