Target Localization Method Based on Parzen Window in Underwater Wireless Sensor Network
PANG Feifei① ZHANG Qunfei① SHI Wentao① HAN Jing① MENG Qingwei②
①(School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China) ②(Information and Navigation College, Air Force Engineering University, Xi’an 710077, China)
In Underwater Wireless Sensor Network (UWSN) the accuracy of target localization suffers from invalid anchors. To reduce the impact, an improved cross-bearing localization method is proposed based on the Parzen window. In this method, the probability of target location is estimated by the Parzen window according to the distribution characteristics of all intersection points, and the target location is selected as the point corresponding to the maximum value of probability. Because of the nonlinear and multi-peak features of the probability distribution, the standard particle swarm optimization method is adopted to solve the problem. Simulations indicate that the proposed method avoids effectively the influence of the invalid anchors on the performance of localization, and has better accuracy and robustness compared with other cross-bearing localization methods in the complex underwater environment.
HSC C C, LIU H H, GOMEZ J L G, et al. Delay-sensitive opportunistic routing for underwater sensor networks[J]. IEEE Sensors Journal, 2015, 15(11): 6584-6591. doi: 10.1109/JSEN.2015.2461652.
[2]
BOSSE J, KRASNOV O, and YAROVOY A. Direct target localization and deghosting in active radar network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(4): 3139-3150. doi: 10.1109/TAES.2015.140170.
[3]
VANDER Hook J, TOKEKAR P, and ISLER V. Algorithms for cooperative active localization of static targets with mobile bearing sensors under communication constraints[J]. IEEE Transactions on Robotics, 2015, 31(4): 864-876. doi: 10.1109/TRO.2015.2432612.
[4]
NOROOZI A and SEBT M A. Target localization from bistatic range measurements in multi-transmitter multi- receiver passive radar[J]. IEEE Signal Processing Letters, 2015, 22(12): 2445-2449. doi: 10.1109/LSP.2015.2491961.
[5]
HE Y, BEHNAD A, and WANG X. Accuracy analysis of the two-reference-node angle-of-arrival localization system[J]. IEEE Wireless Communications Letters, 2015, 4(3): 329-332. doi: 10.1109/LWC.2015.2415788.
[6]
POURSHEIKHLI S and ZAMIRI-JAFARIAN H. TDOA based target localization in inhomogenous underwater wireless sensor network[C]. International Conference on Computer and Knowledge Engineering, Mashhad, Islamic Republic of Iran, 2015: 1-6. doi: 10.1109/ICCKE.2015. 7365873.
[7]
TOMIC S, BEKO M, DINIS R, et al. Efficient estimator for distributed RSS-based localization in wireless sensor networks[C]. IEEE Wireless Communications and Mobile Computing Conference, Dubrovnik, Croatia, 2015: 1266-12751. doi: 10.1109/IWCMC.2015.7289264.
[8]
CHO H and KWON Y. RSS-based indoor localization with PDR location tracking for wireless sensor networks[J]. AEU-International Journal of Electronics and Communications, 2016, 70(3): 250-256. doi: 10.1016/j.aeue. 2015.12.004.
[9]
XU J, MA M, and LAW C L. Cooperative angle-of-arrival position localization[J]. Measurement, 2015, 59(1): 302-313. doi: 10.1016/j.measurement.2014.09.023.
[10]
FARMANI M, PEDERSEN M S, TAN Z H, et al. Maximum likelihood approach to “informed” sound source localization for hearing aid applications[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia, 2015: 16-20. doi: 10.1109/ICASSP.2015.7177923.
WANG Shenshen, FENG Jinfu, WANG Fangnian, et al. Location method of near space radar network based on regularized constrained total least square[J]. Journal of Electronics & Information Technology, 2011, 33(7): 1655-1660. doi: 10.3724/SP.J.1146.2010.01211.
[12]
KUlakowski P, VALES-ALONSO J, EGEA-LOPEZ E, et al. Angle-of-arrival localization based on antenna arrays for wireless sensor networks[J]. Computers & Electrical Engineering, 2010, 36(6): 1181-1186. doi: 10.1016/j. compeleceng.2010.03.007.
HE You, WANG Bencai, WANG Guohong, et al. A clustering localization algorithm with adaptive threshold in passive sensor network[J]. Journal of Astronautics, 2010, 31(4): 1125-1130. doi: 10.3873/j.issn.1000-1328.2010.04.030.
HU Laizhao. Application of passive locating algorithm based on probability in multi-target environment[J]. Electronic Countermeasure Technology, 2002, 17(1): 14-18. doi: 10.3969 /j.issn.1674-2230.2002.01.003.
TAN Kun, CHEN Hong, CAI Xiaoxia, et al. Ghost eliminating algorithm based on probability[J]. Electronic Information Warfare Technology, 2009, 24(5): 29-32. doi: 10.3969/j.issn.1674-2230.2009.05.007.
LIU Han, ZHANG Qing, MENG Lihua, et al. Comprehensive alarm method for equipment conditions based on Parzen window estimation[J]. Journal of Vibration and Shock, 2013, 32(3): 110-114. doi: 10.3969/j.issn.1000-3835.2013.03.022.
[17]
PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065-1076. doi: 10.1214/aoms/1177704472.
[18]
MUSSA H Y, MITCHELL J B O, and AFZAL A M. The Parzen window method: In terms of two vectors and one matrix[J]. Pattern Recognition Letters, 2015, 63: 30-35. doi: 10.1016/j.patrec.2015.06.002.
LI Li and NIU Ben. Particle Swarm Optimization[M]. Beijing: Metallurgical Industry Press, 2009: 25-33.
[21]
EBERHART R C and KENNEDY J. A new optimizer using particle swarm theory[C]. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43. doi: 10.1109/MHS.1995. 494215.
[22]
SHI Y and EBERHART R C. A modified particle swarm optimizer[C]. IEEE International Conference on Evolutionary Computation, Anchorage, AK, USA, 1998: 69-73. doi: 10.1109/ ICEC.1998.699146.