IMPROVING THE ACCURACY OF INDOOR LOCALIZATION USING APPROACH AOA COMBINED THE KALMAN FILTER FOR MASSIVE MIMO SYSTEM

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Authors

  • Duong Thi Hang VNU University of Engineering and Technology

Keywords:

Indoor positioning System; Massive MIMO; Angle Of Arrival; Kalman Filter; Combined AOA and Kalman Filter; NLOS.

Abstract

 In this paper, an efficient improving the accuracy approach of indoor positioning, which is based on AOA combined with a Kalman filter has been proposed. The proposed approach is able to improve positioning accuracy in indoor environments with high accuracy. Two scenarios are created to test the performance of the proposed approach. In the first scenario when the transmission environment has a LOS (Line Of Sight) path and NLOS path, in the second scenario the environment has only the NLOS (Non - Line Of Sight) path because the LOS paths are attenuated. The simulation results show that the proposed approach achieves higher accuracy than the traditional AOA based positioning method. In particular, when the error is less than 2m and the environment has NLOS, the proposed algorithm achieves 20% higher accuracy than the traditional AOA algorithm.

References

[1]. Wen, F., Wymeersch, H., Peng, B., Tay, W. P., So, H. C., & Yang, D. “A survey on 5G massive MIMO localization”. Digital Signal Processing. doi:10.1016/j.dsp.2019.05.005

[2]. Yassin, A.; Nasser, Y.; Awad, M.; Al-Dubai, A.; Liu, R.; Yuen, C.; Raulefs, R.; Aboutanios, E. “Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications”. IEEE Commun. Surv. Tutor 2017, 19, 1327–1346.

[3]. Amar, A., & Weiss, A. J. (n.d.), “Direct position determination of multiple radio signals”, IEEE International Conference on Acoustics, Speech, and Signal Processing. doi:10.1109/icassp.2004.1326199.

[4]. R.G. Stansfield, “Statistical theory of DF fixing”, Journal of IEE 94 (15) (December 1947) 762–770.

[5]. Torrieri, D. (1984). “Statistical Theory of Passive Location Systems”, IEEE Transactions on Aerospace and Electronic Systems, AES-20(2), 183–198. doi:10.1109/taes.1984.310439

[6]. H. Krim, and M. Viberg, “Two Decades of Array Signal Processing Research”, IEEE Signal Processing Magazine, Vol. 13, No. 4, July 1996.

[7]. Marko Malajner, Dušan Gleich, Peter Planinšič, “Angle of Arrival estimation algorithms using Received Signal Strength Indicator”, Journal of Microelectronics, Electronic Components and Materials Vol. 45, No. 4 (2015), 237 – 248.

[8]. Weiss, A. J., & Amar, A, “Direct Position Determination of Multiple Radio Signals”, EURASIP Journal on Advances in Signal Processing, 2005. doi:10.1155/asp.2005.37

[9]. Nil Garcia, Member, IEEE, Henk Wymeersch, Member, IEEE, Erik G. Larsson, Fellow, IEEE, Alexander M. Haimovich, Fellow, IEEE, and Martial Coulon, “Direct Localization for Massive MIMO”, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 65, NO. 10, MAY 15, 2017.

[10]. Zhang, Y., Deng, Z., & Gao, Y. “Angle of Arrival Passive Location Algorithm Based on Proximal Policy Optimization”. Electronics, 8(12), 2019, 1558. doi:10.3390/electronics8121558.

[11]. Lu and Li, “Robot indoor location modeling and simulation based on Kalman filtering”, EURASIP Journal on Wireless Communications and Networking, 2019.

Published

05-02-2021

How to Cite

Hằng. “IMPROVING THE ACCURACY OF INDOOR LOCALIZATION USING APPROACH AOA COMBINED THE KALMAN FILTER FOR MASSIVE MIMO SYSTEM”. Journal of Military Science and Technology, no. 71, Feb. 2021, pp. 57-62, https://en.jmst.info/index.php/jmst/article/view/101.

Issue

Section

Research Articles