自适应关联波门机动群目标跟踪算法
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摘要
为解决中心群跟踪(CGT)算法中由于群机动造成的量测丢失、估计误差增大的问题,提出了一种基于自适应关联波门的机动群目标跟踪算法。首先,将CGT算法与交互式多模型(IMM)算法结合,并利用最新量测信息对IMM算法中的转移概率矩阵进行实时修正。其次,设计了一种用于整体机动和分离机动的自适应关联波门,根据机动时刻模型的新息协方差对其进行自适应调整,确保量测点迹进入波门。仿真结果表明,所提算法一方面减小了传统固定转移概率矩阵带来的估计误差,将优势模型的平均概率由0. 58增加到了0. 7;另一方面,设计的自适应关联波门有效解决了目标机动带来的有效量测减少的问题,相比于传统波门,目标失跟率减少了30%,具备工程实用性。
A new maneuvering group target tracking algorithm based on adaptive correlation gate for solving measurement loss and increasing estimation error of centroid group tracking( CGT) algorithm when tracking maneuvering group target in clutter is proposed in this paper. First,CGT algorithm is combined with interacting multiple model( IMM) algorithm and the latest measurement information is used to modify the transition probability matrix. Second,a new adaptive correlation gate is designed when tracking overall and split maneuvering by the covariance of model innovation to guarantee valid measurements existing in the gate. The simulation results show that the proposed algorithm decreases the estimated error of traditional IMM algorithm with fixed transition probability matrix and increases the probability of dominant model from 0. 58 to 0. 7 on the one hand. On the other hand,the loss-target rate of adaptive gate designed in this paper is reduced by 30% compared to traditional gate on account of decreasing valid measurement during target maneuvering. The proposed algorithm has a certain practical value in engineering.
A new maneuvering group target tracking algorithm based on adaptive correlation gate for solving measurement loss and increasing estimation error of centroid group tracking( CGT) algorithm when tracking maneuvering group target in clutter is proposed in this paper. First,CGT algorithm is combined with interacting multiple model( IMM) algorithm and the latest measurement information is used to modify the transition probability matrix. Second,a new adaptive correlation gate is designed when tracking overall and split maneuvering by the covariance of model innovation to guarantee valid measurements existing in the gate. The simulation results show that the proposed algorithm decreases the estimated error of traditional IMM algorithm with fixed transition probability matrix and increases the probability of dominant model from 0. 58 to 0. 7 on the one hand. On the other hand,the loss-target rate of adaptive gate designed in this paper is reduced by 30% compared to traditional gate on account of decreasing valid measurement during target maneuvering. The proposed algorithm has a certain practical value in engineering.
引文
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[12]郭志,董春云,蔡远利,等.时变转移概率IMM-SRCKF机动目标跟踪算法[J].系统工程与电子技术,2015(1):24-30.GUO Z,DONG C Y,CAI Y L,et al. Time-varying transition probability based IMM-SRCKF algorithm for maneuvering target tracking[J]. System Engineering and Electronics,2015(1):24-30(in Chinese).
[13] GRANSTROM K,WILLETT P,BAR-SHALOM Y. Systematic approach to IMM mixing for unequal dimension states[J].IEEE Transactions on Aerospace and Electronic Systems,2015,51(4):2975-2986.
[14]宗群,王丹丹,邵士凯,等.多无人机协同编队飞行控制研究现状及发展[J].哈尔滨工业大学学报,2017,49(3):1-14.ZONG Q,WANG D D,SHAO S K,et al. Research status and development of multi UAV coordinated formation flight control[J]. Journal of Harbin Institute of Technology,2017,49(3):1-14(in Chinese).
[15] GUO Y,THARMARASA R,RAJAN S,et al. Passive tracking in heavy clutter with sensor location uncertainty[J]. IEEE Transactions on Aerospace and Electronic Systems,2016,52(4):1536-1554.
[16]靳标,纠博,苏涛,等.一种用于杂波中机动目标跟踪的自适应关联波门设计方法[J].西安交通大学学报,2014,48(10):35-41.JIN B,JIU B,SU T,et al. A Method to design adaptive correlating gate for maneuvering target tracking in clutter[J]. Journal of Xi’an Jiaotong University,2014,48(10):35-41(in Chinese).
[17]杜明洋,毕大平,王树亮,等.一种混合的机动群目标分离检测跟踪算法[J].西安交通大学学报,2018,52(10):116-123.DU M Y,BI D P,WANG S L,et al. A hybrid algorithm to detect and track maneuvering group target splitting[J]. Journal of Xi’an Jiaotong University,2018,52(10):116-123(in Chinese).
[2]耿文东,王元钦,董正宏.群目标跟踪[M].北京:国防工业出版社,2014:15-27.GENG W D,WANG Y Q,DONG Z H. Group-targets tracking[M]. Beijing:National Defense Industry Press,2014:15-27(in Chinese).
[3]李昌玺,周焰,郭戈,等.弹道导弹群目标跟踪技术综述[J].战术导弹技术,2015(3):66-73.LI C X,ZHOU Y,GUO G,et al. Summary of group tracking technology based on ballistic missile[J]. Tactical Missile Technology,2015(3):66-73(in Chinese).
[4] JI W Y,ROBERTS S J. Robust measurement validation in target tracking using geometric structure[J]. IEEE Signal Processing Letters,2010,17(5):493-496.
[5] LOPEZ R,DANS P. Low-complexity IMM smoothing for jump markov nonlinear systems[J]. IEEE Transactions on Aerospace and Electronic Systems,2017,53(3):1261-1272.
[6] CHENG T,LI X R,HE Z. Comparison of gating techniques for maneuvering target tracking in clutter[C]∥International Conference on Information Fusion. Piscataway,NJ:IEEE Press,2014:1-8.
[7] WANG M H,WAN Q,YOU Z S. A gate size estimation algorithm for data association filters[J]. Science in China Series F,2008,51(4):425-432.
[8] LI X R,JILKOV V P. Survey of maneuvering target tracking.Part I. Dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems,2004,39(4):1333-1364.
[9] LI X R,JILKOV V P. Survey of maneuvering target tracking.Part V. Multiple-model methods[J]. IEEE Transactions on Aerospace and Electronic Systems,2005,41(4):1255-1321.
[10]周卫东,孙天,储敏,等.交互式多模型粒子滤波优化重采样算法[J].北京航空航天大学学报,2017,43(5):865-871.ZHOU W D,SUN T,CHUN M,et al. Interacting multiple model particle filter optimization resampling algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2017,43(5):865-871(in Chinese).
[11] ZHENG D,WANG S. A new interacting multiple model algorithm for maneuvering target tracking based on adaptive transition probability updating[C]∥IET International Radar Conference. London:IET,2015:5-6.
[12]郭志,董春云,蔡远利,等.时变转移概率IMM-SRCKF机动目标跟踪算法[J].系统工程与电子技术,2015(1):24-30.GUO Z,DONG C Y,CAI Y L,et al. Time-varying transition probability based IMM-SRCKF algorithm for maneuvering target tracking[J]. System Engineering and Electronics,2015(1):24-30(in Chinese).
[13] GRANSTROM K,WILLETT P,BAR-SHALOM Y. Systematic approach to IMM mixing for unequal dimension states[J].IEEE Transactions on Aerospace and Electronic Systems,2015,51(4):2975-2986.
[14]宗群,王丹丹,邵士凯,等.多无人机协同编队飞行控制研究现状及发展[J].哈尔滨工业大学学报,2017,49(3):1-14.ZONG Q,WANG D D,SHAO S K,et al. Research status and development of multi UAV coordinated formation flight control[J]. Journal of Harbin Institute of Technology,2017,49(3):1-14(in Chinese).
[15] GUO Y,THARMARASA R,RAJAN S,et al. Passive tracking in heavy clutter with sensor location uncertainty[J]. IEEE Transactions on Aerospace and Electronic Systems,2016,52(4):1536-1554.
[16]靳标,纠博,苏涛,等.一种用于杂波中机动目标跟踪的自适应关联波门设计方法[J].西安交通大学学报,2014,48(10):35-41.JIN B,JIU B,SU T,et al. A Method to design adaptive correlating gate for maneuvering target tracking in clutter[J]. Journal of Xi’an Jiaotong University,2014,48(10):35-41(in Chinese).
[17]杜明洋,毕大平,王树亮,等.一种混合的机动群目标分离检测跟踪算法[J].西安交通大学学报,2018,52(10):116-123.DU M Y,BI D P,WANG S L,et al. A hybrid algorithm to detect and track maneuvering group target splitting[J]. Journal of Xi’an Jiaotong University,2018,52(10):116-123(in Chinese).