Research on Extreme Learning Machine Model-based Particle Filter Tracking Method for Device-free Localization

doi:10.3969/j.issn.1000-1093.2019.08.025

Abstract

Abstract: Device-free localization (DFL) is an emerging wireless technique for estimating the location of target. The radio frequency signal is seriously polluted due to the uncertainty of wireless channel. An extreme learning machine model-based particle filter (ELM-PF) algorithm for DFL is proposed. The proposed algorithm is used for ELM building （offline stage）and target position estimation (online stage). In the ELM building process, a radio frequency signal propagation model built by ELM is used to describe the mapping relationship between target position and radio signal strength indicator (RSSI). In the process of target position estimation, a target is tracked via particle filter with the radio frequency signal propagation model. Experimental results show that the proposed ELM-PF algorithm can eliminate the fluctuation of the wireless signals and be robust to the tracking accuracy compared with the existing Gaussian process model-particle filter (GPM-PF) and support vector machine-particle filter (SVM-PF). Key

Key words: device-freelocalization, extremelearningmachine, particlefiltering, radiosignalstrength

CLC Number:

TN925⁺.1

GUO Yonghong, SONG Biao, ZHAO Dongyang, LI Xuguang, NAN Huailiang. Research on Extreme Learning Machine Model-based Particle Filter Tracking Method for Device-free Localization[J]. Acta Armamentarii, 2019, 40(8): 1740-1746.

References

［1］ YOUSSEF M, MAH M, AGRAWALA A. Challenges: device-free passive localization for wireless environments[C]∥Proceedings of ACM International Conference on Mobile Computing and Networking. Montreal, Canada: ACM, 2007:222-229.
[2］ WILSION J, PATWARI N. Radio tomographic imaging with wireless networks[J]. IEEE Transactions on Mobile Computing, 2010, 9(5):621-632.
[3］ WILSION J, PATWARI N. See-through walls: motion tracking using variance-based radio tomography networks[J]. IEEE Tran-sactions on Mobile Computing, 2011, 10(5):612-621.
[4］ ZHAO Y, PATWARI N, PHILLIPS J M, et al. Radio tomographic imaging and tracking of stationary and moving people via kernel distance[C]∥Proceedings of ACM/IEEE International Conference on Information Processing in Sensor Networks. Philadelphia, PA, US: IEEE, 2013:229-240.
[5］ YIGITLER H, JANTTI R, KALTIOKALLIO O, et al. Detector based radio tomographic imaging[J]. IEEE Transactions on Mobile Computing, 2018, 17(1):58-71.
[6］ WANG J, GAO Q H, WANG H Y, et al. Time-of-flight-based radio tomography for device free localization[J]. IEEE Transactions on Wireless Communications, 2013, 12(5):2355-2365.
[7］ SABEK I, YOUSSEF M, VASILAKOS A V. ACE: an accurate and efficient multi-entity device-free WLAN localization system[J]. IEEE Transactions on Mobile Computing, 2012, 14(2):261-273.
[8］ SEIFELDIN M, SAEED A, KOSBA A E, et al. Nuzzer: a large-scale device-free passive localization system for wireless environments[J]. IEEE Transactions on Mobile Computing, 2013, 12(7): 1321-1334.
[9］ SHI S Y, SIGG S, CHEN L, et al. Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting[J]. IEEE Transactions on Vehicular Technology, 2018, 67(6): 5217-5230.

[10］ GUO Y, HUANG K D, JIANG N Y, et al. An exponential-Rayleigh model for RSS-based device-free localization and tracking[J]. IEEE Transactions on Mobile Computing, 2015, 14(3):484-494.
[11］ WANG Z, LIU H, XU S, et al. A diffraction measurement model and particle filter tracking method for RSS-based DFL[J]. IEEE Journal on Selected Areas in Communications, 2015, 33(11): 2391-2403.
[12］ XIAO W D, SONG B, YU X T, et al. Nonlinear optimization-based device-free localization with outlier link rejection[J]. Sensors, 2015, 15(4):8072-8087.
[13］宋彪.无设备定位的建模与优化方法研究[D].北京：北京科技大学, 2017.
S0NG B. Research on modeling and optimization approaches for device-free localization[D].Beijing：University of Science and Technology Beijing, 2017. (in Chinese)
[14］ SONG B, WANG H L, XIAO W D, et al. Gaussian process model enabled particle filter for device-free localization[C]∥Proceedings of International Conference on Information Fusion. Xi'an, China: IEEE, 2017:1-6.
[15］ HUANG G B, ZHOU H M, DING X J, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B. Cybernetics, 2012, 42(2):513-529.
[16］ HUANG G, HUANG G B, SONG S, et al. Trends in extreme learning machines: a review[J]. Neural Networks, 2015, 61:32-48.
[17］ XIAO W D, ZHANG J, LI Y J, et al. Class-specific cost regulation extreme learning machine for imbalanced classification[J]. Neurocomputing, 2017, 261:70-82.

第40卷
第8期2019 年8月兵工学报ACTA
ARMAMENTARIIVol.40No.8Aug.2019

[1]	ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine [J]. Acta Armamentarii, 2021, 42(2): 356-369.
[2]	ZHANG Wei， LIU Xing, XU Ai-qiang， PING Dian-fa. Three-layer Multiple Kernel Fault Diagnosis Model with l_p-norm Constraint for Analog Circuit [J]. Acta Armamentarii, 2018, 39(7): 1352-1363.