基于Parzen窗的水下无线传感器网络目标定位方法

庞菲菲; 张群飞; 史文涛; 韩晶; 孟庆微

doi:10.11999/JEIT160246

基于Parzen窗的水下无线传感器网络目标定位方法

doi: 10.11999/JEIT160246

1.
(西北工业大学航海学院西安 710072) ②(空军工程大学信息与导航学院西安 710077)

基金项目:

国家自然科学基金(61531015, 61501374, 61401499)，国家重点实验室基金(9140C230310150C23102)

计量
- 文章访问数: 1453
- HTML全文浏览量: 153
- PDF下载量: 367
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-17
- 修回日期: 2016-08-18
- 刊出日期: 2017-01-19

Target Localization Method Based on Parzen Window in Underwater Wireless Sensor Network

1.
(School of Marine Science and Technology, Northwestern Polytechnical University, Xi&rsquo
2.
(School of Marine Science and Technology, Northwestern Polytechnical University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61531015, 61501374, 61401499), The National Laboratory Foundation of China (9140C230310150C23102)

摘要

摘要: 水声通道复杂多变，使得水下无线传感器网络中节点出现失效的情况，影响了多节点的目标定位性能。为解决这一问题，该文提出一种基于Parzen窗的方位交线定位方法。该方法利用Parzen窗分析所有交点的分布特征，估计目标可能出现在某个位置的概率，将概率最大值对应的点作为目标的估计位置。由于概率分布是非线性、多峰值的，采用带有惯性权重的粒子群算法去求解。仿真实验结果表明，所提方法能够在节点失效的情况下获得较高的目标定位性能，具有较好的鲁棒性。
- 水下无线传感器网络 /
- 目标定位 /
- 方位交线方法 /
- Parzen窗
Abstract: 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.
- Underwater Wireless Sensor Network (UWSN) /
- Target localization /
- Cross-bearing method /
- Parzen window

HTML全文

参考文献(29)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

王燊燊, 冯金富, 王方年, 等. 基于约束最小二乘的近空间雷达网定位算法[J]. 电子与信息学报, 2011, 33(7): 1655-1660. doi: 10.3724/SP.J.1146.2010.01211.

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.

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.

何友, 王本才, 王国宏, 等. 被动传感器组网变门限聚类定位算法[J]. 宇航学报, 2010, 31(4): 1125-1130. doi: 10.3873/ j.issn.1000-1328.2010.04.030.

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.

胡来招. 定位概率计算在多目标环境下的应用[J]. 电子对抗技术, 2002, 17(1): 14-18. doi: 10.3969/j.issn.1674-2230.2002. 01.003.

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.

谭坤, 陈红, 蔡晓霞, 等. 基于概率计算的虚假点消除算法研究[J]. 电子信息对抗技术, 2009, 24(5): 29-32. doi: 10.3969/ j.issn.1674-2230.2009.05.007.

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.

刘晗, 张庆, 孟理华, 等. 基于Parzen窗估计的设备状态综合报警方法[J]. 振动与冲击, 2013, 32(3): 110-114. doi: 10.3969 /j.issn.1000-3835.2013.03.022.

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.

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.

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.

干晓蓉. 模式识别[M]. 昆明: 云南人民出版社, 2006: 43-47.

GAN Xiaorong. Pattern Recognition[M]. Kunming: Yunnan Peoples Publishing House, 2006: 43-47.

李丽, 牛奔. 粒子群优化算法[M]. 北京: 冶金工业出版社, 2009: 25-33.

LI Li and NIU Ben. Particle Swarm Optimization[M]. Beijing: Metallurgical Industry Press, 2009: 25-33.

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.

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.

施引文献

资源附件(0)

访问统计