高斯过程回归下的多机动扩展目标跟踪

doi:10.3969/j.issn.1001-2400.2017.06.006

西安电子科技大学学报

高斯过程回归下的多机动扩展目标跟踪

李翠芸1;王精毅1,2;姬红兵1

(1. 西安电子科技大学电子工程学院，陕西西安 710071；
2. 中国人民解放军95980部队，湖北襄阳 441000)

收稿日期:2016-12-02 出版日期:2017-12-20 发布日期:2018-01-18
作者简介:李翠芸(1976-)，女，副教授，博士，E-mail: cyli@xidian.edu.cn
基金资助:
国家自然科学基金资助项目(61372003)；国家自然科学基金青年资金资助项目(61301289)

Multiple maneuvering extended targets tracking with Gaussian process regression

LI Cuiyun1;WANG Jingyi1,2;JI Hongbing1

(1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
2. Unit 95980, PLA, Xiangyang 441000, China)

Received:2016-12-02 Online:2017-12-20 Published:2018-01-18

摘要/Abstract

摘要：

针对现有多机动扩展目标跟踪算法中形状估计复杂，在考虑杂波的情况下目标跟踪精度不高等问题，提出了一种高斯过程回归下的多机动扩展目标跟踪算法．该算法采用星凸模型对目标进行建模，在单机动扩展目标跟踪算法的基础上引入多目标跟踪算法中的权值参数以实现对多目标的处理，同时利用高斯过程回归对目标形状进行估计．实验仿真表明，所提算法能够对同一场景下多个不同形状的机动扩展目标进行有效跟踪，并且在计算速度、估计精度等方面要优于传统非椭圆机动扩展目标跟踪算法．

关键词: 多机动扩展目标, 星凸模型, 高斯过程回归, 形状估计

Abstract:

In view of the complexity of estimating the shape of the extended target and the low accuracy in multiple maneuvering extended targets tracking in the clutters, a multiple maneuvering extended targets tracking algorithm with Gaussian Process Regression is proposed. First, the extension of targets is modeled as a star-convex model. Then, the concept of weights used in the multiple targets tracking algorithm is introduced to the single maneuvering extended target tracking algorithm to realize multi-targets tracking. Finally, the Gaussian Process Regression is used to estimate the shape for the extended target. Simulation shows that the proposed algorithm is capable of tracking multiple maneuvering extended targets in the same scene with different shapes, and outperforms the traditional non-ellipsoidal extended target tracking algorithm in the estimation precision and computing speed.

Key words: multiple maneuvering extended targets, star-convex models, Gaussian processes regression, shape estimation

李翠芸;王精毅;姬红兵. 高斯过程回归下的多机动扩展目标跟踪[J]. 西安电子科技大学学报, 2017, 44(6): 31-36.

LI Cuiyun;WANG Jingyi;JI Hongbing. Multiple maneuvering extended targets tracking with Gaussian process regression[J]. Journal of Xidian University, 2017, 44(6): 31-36.

参考文献

［1］ BEARD M, REUTER S, GRANSTROM K, et al. Multiple Extended Target Tracking with Labeled Random Finite Sets［J］. IEEE Transactions on Signal Processing, 2016, 64(7): 1638-1653.
［2］ WANG W, ZHOU J, QU X M. A Novel Multiple-model Treatment for Maneuvering Target Tracking［C］//Proceedings of the 19th International Conference on Information Fusion. Piscataway : IEEE, 2016: 31-38.
［3］ LI W L, JIA Y M, DU J P, et al. Gaussian Mixture PHD Filter for Multiple Maneuvering Extended Targets Tracking［C］//Proceedings of the IEEE Conference on Decision and Control. Piscataway: IEEE, 2011: 2410-2415.
［4］李翠芸, 林锦鹏, 姬红兵. 一种基于椭圆RHM的扩展目标Gamma高斯混合CPHD滤波器［J］. 控制与决策, 2015(9): 1551-1558.
LI Cuiyun, LIN Jinpeng, JI Hongbing. A Gamma Gaussian Mixture CPHD Filter for Extended Target Tracking Based on Ellipse Random Hypersurface Models［J］. Control and Decision, 2015(9): 1551-1558.
［5］ BAUM M, HANEBECK U D. Shape Tracking of Extended Objects and Group Targets with Star-convex RHMs［C］//Proceedings of the 14th International Conference on Information Fusion. Piscataway: IEEE, 2011: 5977661.
［6］ LAN J, LI X R. Tracking of Maneuvering Non-ellipsoidal Extended Object or Target Group Using Random Matrix［J］. IEEE Transactions on Signal Processing, 2014, 62(9): 2450-2463.
［7］ BAUM M, HANEBECK U D. Random Hypersurface Models for Extended Object Tracking［C］//Proceedings of the IEEE International Symposium on Signal Processing and Information Technology. Piscataway: IEEE, 2009: 178-183.
［8］ GRANSTROM K, LUNDQUIST C, ORGUNER O. Extended Target Tracking Using a Gaussian-mixture PHD Filter［J］. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286.
［9］ LAN J, LI X R. Tracking of Extended Object or Target Group Using Random Matrix—Part Ⅰ: New Model and Approach［C］//Proceedings of the 15th International Conference on Information Fusion. Piscataway: IEEE, 2012: 2177-2184.
［10］ LAN J, LI X R. Tracking of Extended Object or Target Group Using Random Matrix—Part Ⅱ: Irregular Object［C］//Proceedings of the 15th International Conference on Information Fusion. Piscataway: IEEE, 2012: 2185-2192.
［11］田森平, 周波, 戚其丰. 基于高斯混合PHD滤波的多机动扩展目标跟踪［J］. 中南大学学报: 自然科学版, 2013(12): 4923-4929.
TIAN Shenping, ZHOU Bo, XIAN Qifeng. Gaussian Mixture PHD Filter Based Tracking Multiple Maneuvering Extended Targets［J］. Journal of Central South University: Natural Science Edition, 2013(12): 4923-4929.
［12］ LAN J, LI X R. Joint Tracking and Classification of Non-ellipsoidal Extended Object Using Random Matrix［C］//Proceedings of the 17th International Conference on Information Fusion. Piscataway: IEEE, 2014: 6916029.
［13］李维娟. 机动扩展目标联合跟踪与分类算法研究［D］. 西安: 西安电子科技大学, 2016.
［14］ WAHLSTROM N, OZKAN E. Extended Target Tracking Using Gaussian Processes［J］. IEEE Transactions on Signal Processing, 2015, 63(16): 4165-4178.
［15］王芳黎. 基于高斯过程回归方法的研究及应用［J］. 工业控制计算机, 2015(11): 76-78.
WANG Fangli. Research and Application Based on Gaussian Process Regression［J］. Industrial Control Computer, 2015(11): 76-78.
［16］李翠芸, 王精毅, 姬红兵. 高斯过程回归的CPHD扩展目标跟踪［J］. 西安电子科技大学学报, 2017, 44(3): 8-14.
LI Cuiyun, WANG Jingyi, JI Hongbing. Extended Target Tracking Based on CPHD with Gaussian Process Regression［J］. Journal of Xidian University, 2017, 44(3): 8-14.

高斯过程回归下的多机动扩展目标跟踪

Multiple maneuvering extended targets tracking with Gaussian process regression

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