Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Circuits, Systems, and Signal Processing - Tập 40 - Trang 6325-6339 - 2021
Wenxin Xiong1, Christian Schindelhauer1, Hing Cheung So2, Zhi Wang3
1Department of Computer Science, University of Freiburg, Freiburg, Germany
2Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
3State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

Tóm tắt

We investigate the problem of time-of-arrival (TOA)-based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch M-estimator with a redescending influence function which behaves like $$\ell _0$$ -minimization toward the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios, where the percentage of NLOS paths is not large enough.

Tài liệu tham khảo

A. Beck, P. Stoica, J. Li, Exact and approximate solutions of source localization problems. IEEE Trans. Signal Process. 56(5), 1770–1778 (2008) S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, Cambridge, 2004) T.F. Bechteler, H. Yenigun, 2-D localization and identification based on SAW ID-tags at 2.5 GHz. IEEE Trans. Microwave Theory Technol. 51(5), 1584–1590 (2003) H. Chen, G. Wang, N. Ansari, Improved robust TOA-based localization via NLOS balancing parameter estimation. IEEE Trans. Veh. Technol. 68(6), 6177–6181 (2019) J. Cheon, H. Hwang, D. Kim, Y. Jung, IEEE 802.15.4 ZigBee-based time-of-arrival estimation for wireless sensor networks. Sensors 16(2), 11 (2016) Decawave. DWM1000 datasheet. [Online]. Available https://www.decawave.com/wp-content/uploads/2020/09/DWM1000-Datasheet.pdf I. Guvenc, C.-C. Chong, A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Commun. Surveys Tuts. 11(3), 107–124 (2009) M. Grant, S. Boyd, CVX: MATLAB software for disciplined convex programming, version 2.1. [Online]. Available http://cvxr.com/cvx F. Höflinger, A. Saphala, D.J. Schott, L.M. Reindl, C. Schindelhauer, Passive indoor-localization using echoes of ultrasound signals, in Proc. Int. Conf. Adv. Informat. Technol. (ICAIT). Yangon, Myanmar 2019, pp. 60–65 (2019) J. Liang, D. Wang, L. Su, B. Chen, H. Chen, H.C. So, Robust MIMO radar target localization via nonconvex optimization. Signal Process. 122, 33–38 (2016) W. Liu, P.P. Pokharel, J.C. Principe, Correntropy: Properties and applications in non-Gaussian signal processing. IEEE Trans. Signal Process. 55(11), 5286–5298 (2007) J.J. Moré, Generalizations of the trust region subproblem. Optim. Methods Softw. 2, 189–209 (1993) M. Nikolova, R.H. Chan, The equivalence of half-quadratic minimization and the gradient linearization iteration. IEEE Trans. Image Process. 16(6), 1623–1627 (2007) C.-H. Park, J.-H. Chang, Robust LMedS-based WLS and Tukey-based EKF algorithms under LOS/NLOS mixture conditions. IEEE Access 7, 148198–148207 (2019) J.A. del Peral-Rosado, R. Raulefs, J.A. López-Salcedo, G. Seco-Granados, Survey of cellular mobile radio localization methods: From 1G to 5G. IEEE Commun. Surveys Tuts. 20(2), 1124–1148 (2018) A.R.J. Ruiz, F.S. Granja, Comparing ubisense, bespoon, and decawave UWB location systems: Indoor performance analysis. IEEE Trans. Instrum. Meas. 66(8), 2106–2117 (2017) B.W. Silverman, Density Estimation for Statistics and Data Analysis (Chapman and Hall, London, 1986) H.C. So, Source localization: Algorithms and analysis, in Handbook of Position Location: Theory, Practice and Advances. ed. by S.A. Zekavat, M. Buehrer (Wiley-IEEE Press, New York, 2011) S. Tomic, M. Beko, A bisection-based approach for exact target localization in NLOS environments. Signal Process. 143, 328–335 (2018) S. Tomic, M. Beko, R. Dinis, P. Montezuma, A robust bisection-based estimator for TOA-based target localization in NLOS environments. IEEE Commun. Lett. 21(11), 2488–2491 (2017) R.M. Vaghefi, R.M. Buehrer, Cooperative localization in NLOS environments using semidefinite programming. IEEE Commun. Lett. 19(8), 1382–1385 (2015) V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995) G. Wang, H. Chen, Y. Li, N. Ansari, NLOS error mitigation for TOA-based localization via convex relaxation. IEEE Trans. Wirel. Commun. 13(8), 4119–4131 (2014) F. Xiao, W. Liu, Z. Li, L. Chen, R. Wang, Noise-tolerant wireless sensor networks localization via multinorms regularized matrix completion. IEEE Trans. Veh. Technol. 67(3), 2409–2419 (2018) W. Xiong, H.C. So, TOA-based localization with NLOS mitigation via robust multidimensional similarity analysis. IEEE Signal Process. Lett. 26(9), 1334–1338 (2019) F. Yin, C. Fritsche, F. Gustafsson, A.M. Zoubir, TOA based robust wireless geolocation and Cramer–Rao lower bound analysis in harsh LOS/NLOS environments. IEEE Trans. Signal Process. 61(9), 2243–2255 (2013) F. Yin, C. Fritsche, F. Gustafsson, A.M. Zoubir, EM- and JMAP-ML based joint estimation algorithms for robust wireless geolocation in mixed LOS/NLOS environments. IEEE Trans. Signal Process. 62(1), 168–182 (2014) X.-T. Yuan, B.-G. Hu, Robust feature extraction via information theoretic learning, in Proc. 26th Int. Conf. Mach. Learn., Montreal, QC, Canada, pp. 1193–1200 (2009) Y. Yang, Y. Feng, J. Suykens, Robust low-rank tensor recovery with regularized redescending M-estimator. IEEE Trans. Neural Netw. Learn. Syst. 27(9), 1933–1946 (2015) A.M. Zoubir, V. Koivunen, E. Ollila, M. Muma, Robust Statistics for Signal Processing (Cambridge University Press, Cambridge, 2018) F. Zafari, A. Gkelias, K.K. Leung, A survey of indoor localization systems and technologies. IEEE Commun. Surveys Tuts. 21(3), 2568–2599 (2019) S. Zhang, S. Gao, G. Wang, Y. Li, Robust NLOS error mitigation method for TOA-based localization via second-order cone relaxation. IEEE Commun. Lett. 19(12), 2210–2213 (2015)