多传感器多目标被动跟踪的“鬼点”形成条件

doi:10.12382/bgxb.2021.0686

摘要/Abstract

摘要： 为探究多传感器多目标被动跟踪场景下“鬼点”的形成机理，采用一种集合分析方法，将各角度测量定义为不同的点集，将目标点定义为点集的交，建立多传感多目标被动跟踪问题的数学建模，基于该模型分析得到三类典型跟踪场景下定位到“鬼点”所需几何条件。理论分析表明，传感器观测基线长度、分布特征以及传感器个数是影响“鬼点”生成概率的重要因素。设置3组仿真实验，进一步验证了上述因素对“鬼点”跟踪概率的实际影响。研究结果表明，在求解被动跟踪问题时，适当减小传感器观测基线长度、提高传感器网络相对目标团的空间布局复杂度以及增加传感器数目均能起到一定的抑制“鬼点”作用。

关键词: 多传感器, 多目标, 被动跟踪, 数据关联, 鬼点, 几何条件

Abstract: To explore the formation mechanism of ghost nodes in multi-sensor multi-target passive tracking, a method based on set theory is applied to establish a mathematical model for multi-sensor multi-target passive tracking. The method defines bearing measurements as different point sets and targets as the intersections of them. Using the model, the formation conditions of ghost nodes are obtained for three typical scenarios. It is found that the length of sensors' observation baseline, distribution characteristics, and number of sensors are important factors affecting the generation probability of ghost nodes. Three groups of illustrative simulations are conducted to further verify the above factors' practical effects on the likelihood of ghost nodes being traced. The results indicate reducing the length of sensors' observation baseline appropriately, increasing the spatial layout complexity of the sensor network relative to the target group, and using more sensors for observation have a certain positive effect on suppressing the generation of ghost nodes when solving the multi-sensor multi-target passive tracking problem.

Key words: multi-sensor, multi-target, passivetracking, dataassociation, ghostnodes, geometricconditions

中图分类号:

TP391

李松洲，周荻，杜润乐，刘佳琪，方艺忠. 多传感器多目标被动跟踪的“鬼点”形成条件[J]. 兵工学报, 2022, 43(12): 3103-3112.

LI Songzhou, ZHOU Di, DU Runle, LIU Jiaqi, FANG Yizhong. Conditions for Forming Ghost Nodes in Multi-sensor Multi-target Passive Tracking[J]. Acta Armamentarii, 2022, 43(12): 3103-3112.

参考文献

［1］ LI X H, WILLETT P, BAUM M, et al. PMHT approach for underwater bearing-only multisensor-multitarget tracking in clutter［J］. IEEE Journal of Oceanic Engineering, 2016, 41(4):831-839.
［2］ PATTIPATI K R, DEB S, BAR-SHALOM Y, et al. A new relaxation algorithm and passive sensor data association［J］. IEEE Transactions on Automatic Control, 1992, 37(2):198-213.
［3］ DEB S, PATTIPATI K R, BAR-SHALOM Y. A multisensor-multitarget data association algorithm for heterogeneous sensors［J］. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(2):560-568.
［4］ DEB S, YEDDANAPUDI M, PATTIPATI K, et al. A generalized S-D assignment algorithm for multisensor-multitarget state estimation［J］. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(2):523-538.
［5］ XU B L, WU Z Y. A study on two measurements-to-tracks data assignment algorithms［J］. Information Sciences, 2007, 177(19): 4176-4187.
［6］ MUSICKI D. Multi-target tracking using multiple passive bearings-only asynchronous sensors［J］. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3):1151-1160.
［7］ SATHYAN T, SINHA A. A two-stage assignment-based algorithm for asynchronous multisensor bearings-only tracking［J］. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3):2153-2168.
［8］ BEARD M, ARULAMPALAM S. Comparison of data association algorithms for bearings-only multi-sensor multi-target tracking［C］∥Proceedings of the 2007 10th International Conference on Information Fusion. Quebec, Canada: IEEE, 2007:1-7.
［9］ LANEUVILLE D, HOUSSINEAU J. Passive multi target tracking with GM-PHD filter［C］∥Proceedings of the 2010 13th International Conference on Information Fusion. Edinburgh, UK: IEEE, 2010:1-7.
［10］ WONG S J, VO B T. Square root gaussian mixture PHD filter for multi-target bearings only tracking［C］∥Proceedings of the 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Adelaide, Australia: IEEE, 2011:520-525.
［11］ BISHOP A N, PATHIRANA P N. Localization of emitters via the intersection of bearing lines: a ghost elimination approach［J］. IEEE Transactions on Vehicular Technology, 2007, 56(5):3106- 3110.
［12］ KU B, YOON J, HAN D K, et al. Data association for deghosting in Y-shaped passive linear array sonars［J］. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(1): 103-113.
［13］ YANG R, NG G W, BAR-SHALOM Y. Tracking/fusion and deghosting with doppler frequency from two passive acoustic sensors［C］∥Proceedings of the 16th International Conference on Information Fusion. Istanbul, Turkey: IEEE, 2013:1784-1790.
［14］ KU B, LEE Y, HONG W, et al. Suppressing ghost targets via gating and track history in Y-shaped passive linear array sonars［J］. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3):1605-1616.
［15］ MA N, CHIA C S, YANG R, et al. Target localization by two passive linear arrays［C］∥Proceedings of OCEANS 2006-Asia Pacific. Singapore: IEEE, 2006:1-6.
［16］ MAZUREK P. Deghosting methods for track-before-detect multitarget multisensor algorithms［M］. Poland: InTech, 2008:97-120.
［17］ ATES C A G, ATES A, YLDRM M, et al. A novel data association algorithm for ghost elimination in passive radar systems［C］∥Proceedings of 2019 International Conference on Advanced Technologies for Communications. Hanoi, Vietnam: IEEE, 2019:103-108.
［18］盛丹, 王国宏, 于洪波. 基于Hough变换的测向交叉定位系统多目标跟踪技术［J］. 中国科学:信息科学, 2016, 46(5):651-664.
SHENG D, WANG G H, YU H B. Multi-target tracking in triangulation system based on Hough transform［J］. SCIENTIA SINICA: Informationis, 2016, 46(5):651-664. (in Chinese)
［19］康莉, 谢维信, 黄敬雄. 基于模糊Hough变换的被动传感器系统航迹起始方法［J］. 系统工程与电子技术, 2007, 29(11): 1803-1805.
KANG L, XIE W X, HUANG J X. Track initialization algorithm based on fuzzy Hough transform for passive sensor systems［J］. Systems Engineering and Electronics, 2007, 29(11):1803-1805. (in Chinese)
［20］ BEKIROGLU K, AYAZOGLU M, LAGOA C, et al. An efficient approach to the radar ghost elimination problem［C］∥Proceedings of 2016 American Control Conference. Boston, MA, US: IEEE, 2016:3515-3520.
［21］ HOFFMANN F, CHARLISH A, KOCH W. Trajectory optimization for multi-platform bearing-only tracking with ghosts［C］∥Proceedings of the 2016 19th International Conference on Information Fusion. Heidelberg, Germany: IEEE, 2016:39-44.
［22］ MA F, LU H Z, ZHANG L P, et al. Epipolar geometric association distance: a new cost function for passive sensor data association［J］. IET Radar, Sonar & Navigation, 2021, 15(2):101-110.

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