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.
References
[1]
Geok, T.K., Aung, K.Z., Aung, M.S., et al. (2021) Review of Indoor Positioning: Radio Wave Technology. Applied Sciences, 11, Article 279. https://doi.org/10.3390/app11010279
[2]
Zafari, F., Gkelias, A., Leung, K.K. (2019) A Survey of Indoor Localization Systems and Technologies. IEEE Communications Surveys & Tutorials, 21, 2568-2599. https://doi.org/10.1109/COMST.2019.2911558
[3]
Shang, S. and Wang, L.X. (2022) Overview of WiFi Fingerprinting-Based Indoor Positioning. IET Communications, 16, 725-733. https://doi.org/10.1049/cmu2.12386
[4]
Pinto, B., Barreto, R. and Souto, E. (2021) Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation. IEEE Sensors Journal, 21, 24462-24470. https://doi.org/10.1109/JSEN.2021.3113837
[5]
Subedi, S., Gang, H.S., Ko, N.Y., Hwang, S.S. and Pyun, J.Y. (2019) Improving Indoor Fingerprinting Positioning with Affinity Propagation Clustering and Weighted Centroid Fingerprint. IEEE Access, 7, 31738-31750. https://doi.org/10.1109/ACCESS.2019.2902564
[6]
Zhang, H., Wang, Z., Xia, W., Ni, Y. and Zhao, H. (2022) Weighted Adaptive KNN Algorithm with Historical Information Fusion for Fingerprint Positioning. IEEE Wireless Communications Letters, 11, 1002-1006. https://doi.org/10.1109/LWC.2022.3152610
[7]
Tran, H.Q. and Ha, C. (2020) High Precision Weighted Optimum K-Nearest Neighbors Algorithm for Indoor Visible Light Positioning Applications. IEEE Access, 8, 114597-114607. https://doi.org/10.1109/ACCESS.2020.3003977
[8]
Vanhie-Van Gerwen, J., Geebelen, K., Wan, J., Joseph, W., Hoebeke, J. and De Poorter, E. (2022) Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion. IEEE Transactions on Vehicular Technology, 71, 961-974. https://doi.org/10.1109/TVT.2021.3129917
[9]
Yadav, R.K., Bhattarai, B., Gang, H.-S. and Pyun, J.-Y. (2019) Trusted K Nearest Bayesian Estimation for Indoor Positioning System. IEEE Access, 7, 51484-51498. https://doi.org/10.1109/ACCESS.2019.2910314
[10]
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
[11]
Own, C.-M., Hou, J. and Tao, W. (2019) Signal Fuse Learning Method with Dual Bands WiFi Signal Measurements in Indoor Positioning. IEEE Access, 7, 131805-131817. https://doi.org/10.1109/ACCESS.2019.2940054
[12]
Gligorić, K., Ajmani, M., Vukobratović, D. and Sinanović, S. (2018) Visible Light Communications-Based Indoor Positioning via Compressed Sensing. IEEE Communications Letters, 22, 1410-1413. https://doi.org/10.1109/LCOMM.2018.2833550
[13]
Gong, X., Liu, J., Yang, S., Huang, G. and Bai, Y. (2022) A Usability-Enhanced Smartphone Indoor Positioning Solution Using Compressive Sensing. IEEE Sensors Journal, 22, 2823-2834. https://doi.org/10.1109/JSEN.2021.3137327
[14]
Hu, J., Liu, D., Yan, Z. and Liu, H. (2018) Experimental Analysis on Weight ${K} $-Nearest Neighbor Indoor Fingerprint Positioning. IEEE Internet of Things Journal, 6, 891-897. https://doi.org/10.1109/JIOT.2018.2864607
[15]
Chen, G., Guo, X., Liu, K., Li, X. and Yang, J. (2022) RWKNN: A Modified WKNN Algorithm Specific for the Indoor Localization Problem. IEEE Sensors Journal, 22, 7258-7266. https://doi.org/10.1109/JSEN.2022.3155902
[16]
Liu, X., Zhou, B. and Huang, P. (2016) Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization. IEEE Sensors Journal, 21, 8479-8490. https://doi.org/10.1109/JSEN.2021.3050456
[17]
Feng, C., Au, W.S.A., Valaee, S. and Tan, Z. (2011) Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing. IEEE Transactions on Mobile Computing, 11, 1983-1993. https://doi.org/10.1109/TMC.2011.216
[18]
Hu, J., Liu, H. and Yan, Z. (2019) Adaptive Affinity Propagation Algorithm Based on New Strategy of Dynamic Damping Factor and Preference. IEEJ Transactions on Electrical and Electronic Engineering, 14, 97-104. https://doi.org/10.1002/tee.22792
[19]
Hu, J. and Hu, C. (2023) A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter. IEEE Access, 11, 32907-32918. https://doi.org/10.1109/ACCESS.2023.3263583
