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Chen, Z., Zou, H., Yang, J., Jiang, H. and Xie, L. (2019) WiFi Fingerprinting Indoor Localization Using Local Feature-Based Deep LSTM. IEEE Systems Journal, 14, 3001-3010.
https://doi.org/10.1109/JSYST.2019.2918678

has been cited by the following article:

• 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.