Data Association Algorithm for Multi-target Tracking of Underwater Bearings-only Systems with Double Observation Stations
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摘要: 针对双观测站纯方位系统在定位多个目标时, 产生的大量虚假定位点使得量测数据依据目标数量和虚假量测数量都呈平方倍数增长的问题, 文中在最近邻域法(NNF)的基础上增加了一组事件, 考虑了跟踪门内不存在有效量测的情况, 改进得出修正最近邻域法(MNNF); 简化了联合概率数据互联算法(JPDA)的计算步骤, 对公共区域中的量测统一做弱加权, 提出了修正概率数据关联算法(MPDA), 并对以上方法做了仿真及比较。结果表明, 利用该2种方法在双观测站纯方位系统存在“鬼点”的情况下有较为理想的估计结果, 并且MPDA算法的效果明显优于MNNF算法, 可有效跟踪目标。Abstract: The bearings-only system with double observation stations generates a mass of false-location while locating multiple targets, which result in squared growth in the number of target measurement data and the number of false measurements. To solve this problem, this paper proposes modified nearest neighbor filter(MNNF) by adding a set of events according to nearest neighbor filter(NNF) with consideration of the fact that there is no effective measurement in the tracking gate. Then a modified probabilistic data association(MPDA) algorithm is also proposed by simplifying the calculation procedure of the joint probabilistic data association(JPDA) algorithm and weakly weighting all of the measurements in common area. Simulation is conducted to compare MPDA and MNNF, and the results show that both algorithms can help obtain satisfactory estimation result in the existence of “ghost points” in the bearings-only system with double observation stations, but MPDA performs obviously better than MNNF.
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[1] Gavish M, Weiss A J. Performance Analysis of Bearing-only Target Location Algorithms[J]. IEEE Transactions on Aerospace & Electronic Systems, 1992, 28(3): 817-828. [2] 夏佩伦. 纯方位目标跟踪系统的可观测程度[J]. 火力与指挥控制, 1992, (2): 25-31. [3] 石章松, 刘忠. 单站纯方位目标跟踪系统可观测性分析[J]. 火力与指挥控制, 2007, 32(2): 26-29.Shi Zhang-song, Liu Zhong. The Analysis of the Observability on the Single Platform Bearings-only Target Tracking System[J]. Fire Control and Command Control, 2007, 32(2): 26-29. [4] 刘健, 刘忠, 玄兆林. 纯方位两站协同目标运动分析算法研究[J]. 舰船科学技术, 2006, 28(1): 64-69.Liu Jian, Liu Zhong, Xuan Zhao-lin. Algorithms of Bearings-only Target Motion Analysis by Two Observers[J]. Ship Science and Technology, 2006, 28(1):64-69. [5] Ho K C, Chan Y T. An Asymptotically Unbiased Estimator for Bearings-only and Doppler-bearing Target Motion Analysis[J]. IEEE Transactions on Signal Processing, 2006, 54(3): 809-822. [6] Nardone S C, Aidala V J. Observability Criteria for Bearings-Only Target Motion Analysis[J]. IEEE Transactions on Aerospace & Electronic Systems, 1981, AES-17(2): 162-166. [7] Singer R A, Sea R G. A New Filter for Optimal Tracking in Dense Multitarget Enviroment[C]//The Ninth Allerton Conference Circuit and System Theory: Illinois, USA, 1971: 201-211. [8] Song T L, Dong G L, Ryu J. A Probabilistic Nearest Neighbor Filter Algorithm for Tracking in a Clutter Environment[J]. Signal Processing, 2013, 85(10): 2044-2053. [9] Boumediene M, Ouamri A, Dahnoun N. Lane Boundary Detection and Tracking using NNF and HMM Approaches[C]//2007 IEEE Intelligent Vehicles Symposium. San Diego: IEEE, 2007. [10] Bar-Shalom Y, Tse E. Tracking in Cluttered Environment with Probabilistic Data Association[J]. Automatica, 1975, 11(5): 451-460. [11] Chang K C, Bar-Shalom Y. Distributed Adaptive Estimation with Probabilistic Data Association[J]. Automatica, 1989, 25(3): 359-369. [12] Kirubarajan T, Bar-Shalom Y. Probabilistic Data Association Techniques for Target Tracking in Clutter[J]. Proceedings of the IEEE, 2004, 92(3): 536-557. [13] 吴佳芯. 多目标跟踪的数据关联算法研究[D]. 西安: 西安电子科技大学, 2013. [14] Bar-Shalom Y. Tracking and Data Association[M]. San Diego, CA: Academic Press Professional, 1987. [15] Musicki D, Evans R. Joint Integrated Probabilistic Data Association: JIPDA[J]. IEEE Transactions on Aerospace & Electronic Systems, 2004, 40(3): 1093-1099. [16] Song T L, Kim H W, Musicki D. Iterative Joint Integrated Probabilistic Data Association for Multitarget tracking[J]. IEEE Transactions on Aerospace & Electronic Systems, 2015, 51(1): 642-653. [17] 郭晶, 罗鹏飞. 密集杂波环境下的数据关联快速算法[J]. 航空学报, 1998, 19(3): 305-309.Guo Jing, Luo Peng-fei. Fast Algorithm for Data Association in Dense Clutter[J]. Acta Aeronautica et Astronautica Sinica, 1998, 19(3): 305-309. [18] 刘俊, 刘瑜, 何友,等. 杂波环境下基于全邻模糊聚类的联合概率数据互联算法[J]. 电子与信息学报, 2016, 38(6): 1438-1445.Liu Jun, Liu Yu, He You, et al. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1438-1445. [19] Julier S, Uhlmann J, Durrantwhyte H F. A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators[J]. IEEE Trans Automatic Control, 2000, 45(3): 477-482.
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