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摘要: 多目标跟踪(MTT)是雷达数据处理领域的难点。相较于一般场景，海上多目标跟踪(MMTT)面临的挑战更大。一方面，复杂的海洋环境和较低的信杂比使得海面小型目标的检测性能受限，检测得到的点迹存在漏检并包含大量虚警，导致多目标跟踪处理的难度大大增加；另一方面，当海面目标以多群形式编队运动，或采用高分辨率雷达对海探测时，目标量测容易呈现跨单元分布的特征，这种情况下，采用常规的多目标跟踪方法效果不理想。目前，国内外关于海上多目标跟踪方面的研究文献还不多，且大都侧重于单一情形。该文从常规多目标跟踪方法、幅度信息辅助的多目标跟踪方法、多目标检测前跟踪方法以及多扩展目标跟踪方法等4个方面对海上多目标跟踪技术进行了梳理，并对海上多目标跟踪的未来发展方向进行了展望。Abstract: Multi-Target Tracking (MTT) is a difficult task in radar data processing. When compared to tracking in various fields or scenario, Maritime MTT (MMTT) is a challenging one and also a daunting task. On one hand, low signal-to-clutter ratio in the highly complex marine environment limits the detection performance for small targets at sea, and the plots obtained by the detector contain missing detections and a large number of false alarms, which make MTT much more difficult. On the other hand, when marine targets are moving in the form of multiple groups, or when the high resolution radar is used in marine detection applications, the measurements of the target pave the way to show efficiently the distribution characteristics of occupying multiple cells. In this case, using of conventional MTT methods is not ideal as their performance is not effective as desired. Currently, the number of papers on MMTT at home and abroad is very limited, and most of them only focus on a single target. This paper summarizes the use of MMTT algorithms based on four methods: conventional MTT method, amplitude aided MTT method, multi-target track-before-detect method, and multiple extended target-tracking method. In addition, this paper also considers and analyzes the future perspective of MMTT comprehensively.
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[1] VO B N, MALLICK M, BAR-SHALOM Y, et al. Multitarget Tracking[M]. WEBSTER J G. Wiley Encyclopedia of Electrical and Electronics Engineering. New York: John Wiley & Sons, 2015. [2] REID D. An algorithm for tracking multiple targets[J]. IEEE Transactions on Automatic Control, 1979, 24(6): 843–854. doi: 10.1109/TAC.1979.1102177 [3] FORTMANN T E, BAR-SHALOM Y, and SCHEFFE M. Sonar tracking of multiple targets using joint probabilistic data association[J]. IEEE Journal of Oceanic Engineering, 1983, 8(3): 173–184. doi: 10.1109/JOE.1983.1145560 [4] MAHLER R P S. Statistical Multisource-Multitarget Information Fusion[M]. Boston: Artech House, 2007. [5] DAUM F and HUANG J. Nonlinear filters with log-homotopy[C]. SPIE, Signal and Data Processing of Small Targets USA, 2007, San Diego, 2007: 5920–5923. [6] CHANG K C and BAR-SHALOM Y. Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers[J]. IEEE Transactions on Automatic Control, 1984, 29(7): 585–594. doi: 10.1109/TAC.1984.1103597 [7] BEARD M, VO B T, and VO B N. Bayesian multi-target tracking with merged measurements using labelled random finite sets[J]. IEEE Transactions on Signal Processing, 2015, 63(6): 1433–1447. doi: 10.1109/TSP.2015.2393843 [8] KOCH J W. Bayesian approach to extended object and cluster tracking using random matrices[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042–1059. doi: 10.1109/TAES.2008.4655362 [9] MAHLER R. PHD filters for nonstandard targets, I: Extended targets[C]. 2019 12th International Conference on Information Fusion, Seattle, USA, 2009: 915–921. [10] VO B N, VO B T, PHAM N T, et al. Joint detection and estimation of multiple objects from image observations[J]. IEEE Transactions on Signal Processing, 2010, 58(10): 5129–5141. doi: 10.1109/TSP.2010.2050482 [11] HOSEINNEZHAD R, VO B N, VO B T, et al. Visual tracking of numerous targets via multi-Bernoulli filtering of image data[J]. Pattern Recognition, 2012, 45(10): 3625–3635. doi: 10.1016/j.patcog.2012.04.004 [12] MAHLER R. CPHD filters for superpositional sensors[C]. SPIE, Signal and Data Processing of Small Targets 2009, San Diego, USA, 2009: 74450E1–74450E12. [13] NANNURU S, COATES M, and MAHLER R. Computationally-tractable approximate PHD and CPHD filters for superpositional sensors[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 410–420. doi: 10.1109/JSTSP.2013.2251605 [14] BAR-SHALOM Y and TSE E. Tracking in a cluttered environment with probabilistic data association[J]. Automatica, 1975, 11(5): 451–460. doi: 10.1016/0005-1098(75)90021-7 [15] MUSICKI D and EVANS R. Joint integrated probabilistic data association: JIPDA[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3): 1093–1099. doi: 10.1109/TAES.2004.1337482 [16] BLACKMAN S S and POPOLI R. Design and Analysis of Modern Tracking Systems[M]. Boston: Artech House, 1999: 1–24. [17] KURIEN T. Issues in the Design of Practical Multitarget Tracking Algorithms[M]. Bar-Shalom Y. Multitarget-Multisensor Tracking: Advanced Applications. Norwood: Artech House, 1990: 43–83. [18] SVENSSON D. Target tracking in complex scenarios[D]. Chalmers University of Technology, 2010. [19] SATHYAN T, SINHA A, KIRUBARAJAN T, et al. MDA-based data association with prior track information for passive multitarget tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 539–556. doi: 10.1109/TAES.2011.5705690 [20] THARMARASA R, SUTHARSAN S, KIRUBARAJAN T, et al. Multiframe assignment tracker for MSTWG data[C]. 2009 12th International Conference on Information Fusion, Seattle, USA, 2009: 1837–1844. [21] MAHLER R P S, VO B T, and VO B N. CPHD filtering with unknown clutter rate and detection profile[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3497–3513. doi: 10.1109/TSP.2011.2128316 [22] LIAN Feng, HAN Chongzhao, and LIU Weifeng. Estimating unknown clutter intensity for PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 2066–2078. doi: 10.1109/TAES.2010.5595616 [23] CHEN Xin, THARMARASA R, PELLETIER M, et al. Integrated clutter estimation and target tracking using Poisson point processes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1210–1235. doi: 10.1109/TAES.2012.6178058 [24] VO B T, VO B N, HOSEINNEZHAD R, et al. Robust multi-Bernoulli filtering[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 399–409. doi: 10.1109/JSTSP.2013.2252325 [25] BATTISTELLI G, CHISCI L, FANTACCI C, et al. Consensus CPHD filter for distributed multitarget tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 508–520. doi: 10.1109/JSTSP.2013.2250911 [26] YI Wei, LI Suqi, WANG Bailu, et al. Computationally efficient distributed multi-sensor fusion with multi-Bernoulli filter[J]. IEEE Transactions on Signal Processing, 2019, 68: 241–256. [27] LI Siqi, YI Wei, HOSEINNEZHAD R, et al. Robust distributed fusion with labeled random finite sets[J]. IEEE Transactions on Signal Processing, 2018, 66(2): 278–293. doi: 10.1109/TSP.2017.2760286 [28] MAHLER R P S. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152–1178. doi: 10.1109/TAES.2003.1261119 [29] SINGH S S, VO B N, BADDELEY A, et al. Filters for spatial point processes[J]. SIAM Journal on Control and Optimization, 2009, 48(4): 2275–2295. doi: 10.1137/070710457 [30] ERDINC O, WILLETT P, and BAR-SHALOM Y. The Bin-occupancy filter and its connection to the PHD filters[J]. IEEE Transactions on Signal Processing, 2009, 57(11): 4232–4246. doi: 10.1109/TSP.2009.2025816 [31] VO B N and MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091–4104. doi: 10.1109/TSP.2006.881190 [32] VO B N, SINGH S, and DOUCET A. Sequential Monte Carlo methods for multitarget filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224–1245. doi: 10.1109/TAES.2005.1561884 [33] CLARK D and BELL J. Convergence results for the particle PHD filter[J]. IEEE Transactions on Signal Processing, 2006, 54(7): 2652–2661. doi: 10.1109/TSP.2006.874845 [34] JOHANSEN A M, SINGH S S, DOUCET A, et al. Convergence of the SMC implementation of the PHD filte[J]. Methodology and Computing in Applied Probability, 2006, 8(2): 265–291. doi: 10.1007/s11009-006-8552-y [35] WHITELEY N, SINGH S, and GODSILL S. Auxiliary particle implementation of probability hypothesis density filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1437–1454. doi: 10.1109/TAES.2010.5545199 [36] RISTIC B, CLARK D, VO B N, et al. Adaptive target birth intensity for PHD and CPHD filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1656–1668. doi: 10.1109/TAES.2012.6178085 [37] MAHLER R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523–1543. doi: 10.1109/TAES.2007.4441756 [38] VO B T, VO B N, and CANTONI A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553–3567. doi: 10.1109/TSP.2007.894241 [39] PASHA S A, VO B N, TUAN H D, et al. A Gaussian mixture PHD filter for jump Markov system models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919–936. doi: 10.1109/TAES.2009.5259174 [40] MAHLER R. The Multisensor PHD filter: II. Erroneous solution via “Poisson magic”[C]. SPIE, Signal Processing, Sensor Fusion, and Target Recognition XVIII, Orlando, USA, 2009: 73360D-12. [41] MAHLER R. Approximate multisensor CPHD and PHD filters[C]. 2010 13th International Conference on Information Fusion, Edinburgh, UK, 2010: 1–8. [42] VO B T, VO B N, and CANTONI A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 409–423. doi: 10.1109/TSP.2008.2007924 [43] VO B T and VO B N. Labeled random finite sets and multi-object conjugate priors[J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3460–3475. doi: 10.1109/TSP.2013.2259822 [44] VO B N, VO B T, and PHUNG D. Labeled random finite sets and the Bayes multi-target tracking filter[J]. IEEE Transactions on Signal Processing, 2014, 62(24): 6554–6567. doi: 10.1109/TSP.2014.2364014 [45] REUTER S, VO B T, VO B N, et al. The labeled multi-Bernoulli filter[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3246–3260. doi: 10.1109/TSP.2014.2323064 [46] VO B N, VO B T, REUTER S, et al. Towards large scale multi-target tracking[C]. SPIE, Sensors and Systems for Space Applications VII, Baltimore, USA, 2014: 90850W. [47] 林两魁, 许丹, 盛卫东, 等. 基于随机有限集的中段弹道目标群星载红外像平面跟踪方法[J]. 红外与毫米波学报, 2010, 29(6): 465–470.LIN Liangkui, XU Dan, SHENG Weidong, et al. Tracking of midcourse ballistic target group with space-based infrared FPA based on random finite set[J]. Journal of Infrared and Millimeter Waves, 2010, 29(6): 465–470. [48] 王晓, 韩崇昭, 连峰. 基于随机有限集的目标跟踪方法研究及最新进展[J]. 工程数学学报, 2012, 29(4): 567–578.WANG Xiao, HAN Chongzhao, and LIAN Feng. Survey of target tracking based on random finite set[J]. Chinese Journal of Engineering Mathematics, 2012, 29(4): 567–578. [49] 杜航原, 赵玉新, 杨永鹏, 等. 基于随机有限集的SLAM算法[J]. 系统工程与电子技术, 2012, 34(7): 1452–1457.DU Hangyuan, ZHAO Yuxin, YANG Yongpeng, et al. Slam algorithm based on random finite set[J]. Systems Engineering and Electronics, 2012, 34(7): 1452–1457. [50] 徐洋, 徐晖, 罗少华, 等. 基于随机有限集理论的多传感器目标联合检测跟踪算法[J]. 国防科技大学学报, 2013, 35(1): 89–96.XU Yang, XU Hui, LUO Shaohua, et al. Multisensor joint target detection and tracking algorithm based on random finite sets[J]. Journal of National University of Defense Technology, 2013, 35(1): 89–96. [51] 吴静静. 基于随机有限集的视频目标跟踪算法研究[D]. [博士论文], 上海交通大学, 2012.WU Jingjing. Research on tracking algorithm for visual targets based on random finite set[D]. [Ph. D. dissertation], Shanghai Jiao Tong University, 2012. [52] XU Jing, WANG Xiukun, HU Jiasheng, et al. Multi-platform bearings-only tracking fusion of maritime targets[C]. CIE International Conference on Radar Proceedings, Beijing, China, 2001: 1112–1114. [53] STATECZNY A and KAZIMIERSKI W. A concept of decentralized fusion of maritime radar targets with multisensor Kalman filter[C]. International Radar Symposium, Vilnius, Lithuania, 2010: 1–4. [54] GADE B, KLOSTER M, and ARONSEN M. Non-elliptical validation gate for maritime target tracking[C]. 2018 21st International Conference on Information Fusion, Cambridge, UK, 2018: 1301–1308. [55] WEI Yali, ZHU Daqi, and CHU Zhenzhong. Underwater dynamic target tracking of autonomous underwater vehicle based on MPC algorithm[C]. 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Application, Wuhan, China, 2018: 1–5. [56] LERRO D and BAR-SHALOM Y. Automated tracking with target amplitude information[C]. 1990 American Control Conference, San Diego, USA, 1990: 2875–2880. [57] EHRMAN L M, BURTON C, and BLAIR W D. Using target RCS to aid measurement-to-track association in multi-target tracking[C]. 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory, Cookeville, USA, 2006: 89–93. [58] CLARK D, RISTIC B, VO B N, et al. Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR[J]. IEEE Transactions on Signal Processing, 2010, 58(1): 26–37. doi: 10.1109/TSP.2009.2030640 [59] BREKKE E F, HALLINGSTAD O, and GLATTETRE J H. Performance of PDAF-based tracking methods in heavy-tailed clutter[C]. 2009 12th International Conference on Information Fusion, Seattle, USA, 2009: 6–9. [60] BREKKE E F, HALLINGSTAD O, and GLATTETRE J H. The modified Riccati equation for amplitude-aided target tracking in heavy-tailed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(4): 2874–2886. doi: 10.1109/TAES.2011.6034670 [61] LI Suqi, KONG Lingjiang, YI Wei, et al. PHD filter with amplitude information in Weibull clutter[C]. 2013 IEEE Radar Conference, Ottawa, Canada, 2013: 1–6. [62] BUZZI S, LOPS M, VENTURINO L, et al. Track-before-detect procedures in a multi-target environment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1135–1150. doi: 10.1109/TAES.2008.4655369 [63] GROSSI E, LOPS M, VENTURINO L, et al. A novel dynamic programming algorithm for track-before-detect in radar systems[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2608–2619. doi: 10.1109/TSP.2013.2251338 [64] YI Wei, MORELANDE M R, KONG Lingjiang, et al. An efficient multi-frame track-before-detect algorithm for multi-target tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 421–434. doi: 10.1109/JSTSP.2013.2256415 [65] GROSSI E, LOPS M, and VENTURINO L. A track-before-detect algorithm with thresholded observations and closely-spaced targets[J]. IEEE Signal Processing Letters, 2013, 20(12): 1171–1174. doi: 10.1109/LSP.2013.2283586 [66] WANG Jinghe, YI Wei, KIRUBARAJAN T, et al. An efficient recursive multiframe track-before-detect algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 190–204. doi: 10.1109/TAES.2017.2741898 [67] YI Wei, FANG Zicheng, LI Wujun, et al. Multi-frame track-before-detect algorithm for maneuvering target tracking[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4104–4118. doi: 10.1109/TVT.2020.2976095 [68] BOERS Y and DRIESSEN J N. Multitarget particle filter track before detect application[J]. IEE Proceedings-Radar, Sonar and Navigation, 2004, 151(6): 351–357. doi: 10.1049/ip-rsn:20040841 [69] ORTON M and FITZGERALD W. A Bayesian approach to tracking multiple targets using sensor arrays and particle filters[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 216–223. doi: 10.1109/78.978377 [70] KREUCHER C, KASTELLA K, and HERO A O. Multitarget tracking using the joint multitarget probability density[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1396–1414. doi: 10.1109/TAES.2005.1561892 [71] Garcia-Fernandez A F, Grajal J, and Morelande M R. Two-layer particle filter for multiple target detection and tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 1569–1588. doi: 10.1109/TAES.2013.6558005 [72] YI Wei, MORELANDE M R, KONG Lingjiang, et al. A computationally efficient particle filter for multitarget tracking using an independence approximation[J]. IEEE Transactions on Signal Processing, 2013, 61(4): 843–856. doi: 10.1109/TSP.2012.2229999 [73] ÚBEDA-MEDINA L, GARCÍA-FERNANDEZ Á F, and GRAJAL J. Adaptive auxiliary particle filter for track-before-detect with multiple targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(5): 2317–2330. doi: 10.1109/TAES.2017.2691958 [74] STREIT R L, GRAHAM M L, and WALSH M J. Multitarget tracking of distributed targets using histogram-PMHT[J]. Digital Signal Processing, 2002, 12(2/3): 394–404. [75] DAVEY S J. Histogram PMHT with particles[C]. 14th International Conference on Information Fusion, Chicago, USA, 2011: 779–786. [76] PUNITHAKUMAR K and KIRUBARAJAN T. A sequential Monte Carlo probability hypothesis density algorithm for multitarget track-before-detect[C]. The SPIE, Signal and Data Processing of Small Targets, San Diego, USA, 2005: 59131S. [77] LIN L, BAR-SHALOM Y, and KIRUBARAJAN T. Track labeling and PHD filter for multitarget tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 778–795. doi: 10.1109/TAES.2006.248213 [78] PAPI F, VO B T, BOCQUEL M, et al. Multi-target track-before-detect using labeled random finite set[C]. International Conference on Control, Automation and Information Science (ICCAIS), Nha Trang, Vietnam, 2013: 116–121. [79] GARCIA-FERNANDEZ A F. Track-before-detect labeled multi-Bernoulli particle filter with label switching[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(5): 2123–2138. doi: 10.1109/TAES.2016.150343 [80] PAPI F, VO B N, VO B T, et al. Generalized labeled multi-Bernoulli approximation of multi-object densities[J]. IEEE Transactions on Signal Processing, 2015, 63(20): 5487–5497. doi: 10.1109/TSP.2015.2454478 [81] MAHLER R and EL-FALLAH A. An approximate CPHD filter for superpositional sensors[C]. SPIE, Sensor Fusion, and Target Recognition XXI, Baltimore, USA, 2012: 83920K. [82] LI Suqi, YI Wei, HOSEINNEZHAD R, et al. Multiobject tracking for generic observation model using labeled random finite sets[J]. IEEE Transactions on Signal Processing, 2018, 66(2): 368–383. doi: 10.1109/TSP.2017.2764864 [83] 童慧思, 张颢, 孟华东, 等. PHD滤波器在多目标检测前跟踪中的应用[J]. 电子学报, 2011, 39(9): 2046–2051.TONG Huisi, ZHANG Hao, MENG Huadong, et al. Probability hypothesis density filter multitarget track-before-detect application[J]. Acta Electronica Sinica, 2011, 39(9): 2046–2051. [84] 林再平, 周一宇, 安玮, 等. 基于概率假设密度滤波平滑器的检测前跟踪算法[J]. 光学学报, 2012, 32(10): 1012003. doi: 10.3788/AOS201232.1012003LIN Zaiping, ZHOU Yiyu, AN Wei, et al. Track-before-detect algorithm based on probability hypothesis density smoother[J]. Acta Optica Sinica, 2012, 32(10): 1012003. doi: 10.3788/AOS201232.1012003 [85] LI Suqi, WANG Bailu, YI Wei, et al. Multiple sensor multi-Bernoulli filter based track-before-detect for polarimetric MIMO radars[C]. 2014 IEEE Radar Conference, Cincinnati, USA, 2014: 1562–1266. [86] 李翠芸, 李宁, 姬红兵. 多伯努利滤波的快速红外弱小目标检测与跟踪[J]. 西安电子科技大学学报: 自然科学版, 2016, 43(4): 69–74, 109. doi: 10.3969/j.issn.1001-2400.2016.04.013LI Cuiyun, LI Ning, and JI Hongbing. Fast IR dim small targets detection and tracking based on the multi-Bernoulli filter[J]. Journal of Xidian University, 2016, 43(4): 69–74, 109. doi: 10.3969/j.issn.1001-2400.2016.04.013 [87] 李发宗, 毛兴鹏, 常维国. 利用极化信息的高频地波雷达TBD检测算法[J]. 哈尔滨工业大学学报, 2016, 48(5): 36–42. doi: 10.11918/j.issn.0367-6234.2016.05.005LI Fazong, MAO Xingpeng, and CHANG Weiguo. TBD algorithm based on polarization information of high frequency surface wave radar[J]. Journal of Harbin Institute of Technology, 2016, 48(5): 36–42. doi: 10.11918/j.issn.0367-6234.2016.05.005 [88] 裴家正, 黄勇, 董云龙, 等. 杂波背景下基于概率假设密度的辅助粒子滤波检测前跟踪改进算法[J]. 雷达学报, 2019, 8(3): 355–365. doi: 10.12000/JR18060PEI Jiazheng, HUANG Yong, DONG Yunlong, et al. Track-before-detect algorithm based on improved auxiliary particle PHD filter under clutter background[J]. Journal of Radars, 2019, 8(3): 355–365. doi: 10.12000/JR18060 [89] FARSHCHIAN M and RAJ R G. A multi-scale and adaptive track-before-detect technique for maritime environments[C]. 2011 IEEE RadarCon, Kansas City, USA, 2011: 818–823. [90] MCDONALD M and BALAJI B. Impact of measurement model mismatch on nonlinear track-before-detect performance for maritime RADAR surveillance[J]. IEEE Journal of Oceanic Engineering, 2011, 36(4): 602–614. doi: 10.1109/JOE.2011.2165369 [91] EBENEZER S P and PAPANDREOU-SUPPAPPOLA A. Multiple target track-before-detect in compound Gaussian clutter[C]. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, 2015: 2539–2543. [92] ERRASTI-ALCALA B, FUSCALDO W, BRACA P, et al. Realistic extended target model for track before detect in maritime surveillance[C]. OCEANS 2015, Genoa, Italy, 2015: 1–9. [93] JIANG Haichao, YI Wei, CUI Guolong, et al. Knowledge-based track-before-detect strategies for fluctuating targets in K-distributed clutter[J]. IEEE Sensors Journal, 2016, 16(19): 7124–7132. doi: 10.1109/JSEN.2016.2597320 [94] BERRY P, VENKATARAMAN K, and ROSENBERG L. Adaptive detection of low-observable targets in correlated sea clutter using Bayesian track-before-detect[C]. 2017 IEEE Radar Conference, Seattle, USA, 2017: 398–403. [95] GILHOLM K, GODSILL S, MASKELL S, et al. Poisson models for extended target and group tracking[C]. SPIE, Signal and Data Processing of Small Targets, San Diego, USA, 2005: 230–241. [96] GRANSTRÖM K, LUNDQUIST C, and ORGUNER O. Extended target tracking using a Gaussian-mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268–3286. doi: 10.1109/TAES.2012.6324703 [97] CLARK D and GODSILL S. Group target tracking with the Gaussian mixture probability hypothesis density filter[C]. 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, Australia, 2007: 149–154. [98] KOCH W and SAUL R. A Bayesian approach to extended object tracking and tracking of loosely structured target groups[C]. 2005 7th International Conference on Information Fusion, Philadelphia, PA, USA,2005: 827–834. doi: 10.1109/ICIF.2005.1591939. [99] BAUM M and HANEBECK U D. Random hypersurface models for extended object tracking[C]. 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates, 2009: 178–183. [100] BAUM M and HANEBECK U D. Extended object tracking with random hypersurface models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 149–159. doi: 10.1109/TAES.2013.120107 [101] GRANSTRÖM K and ORGUNER U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(11): 5657–5671. doi: 10.1109/TSP.2012.2212888 [102] GRANSTRÖM K and ORGUNER U. Implementation of the GIW-PHD filter[R]. LiTH-ISY-R-3046, 2012. [103] ZHANG Hui, XU Hui, WANG Xueying, et al. A PHD filter for tracking closely spaced objects with elliptic random hypersurface models[C]. The 16th International Conference on Information Fusion, Istanbul, Turkey, 2013: 1558–1565. [104] GRANSTRÖM K and LUNDQUIST C. On the use of multiple measurement models for extended target tracking[C]. The 16th International Conference on Information Fusion, Istanbul, Turkey, 2013: 1534–1541. [105] GRANSTRÖM K, REUTER S, MEISSNER D, et al. A multiple model PHD approach to tracking of cars under an assumed rectangular shape[C]. 17th International Conference on Information Fusion, Salamanca, Spain, 2014: 1–8. [106] GRANSTRÖM K and ORGUNER U. Estimation and maintenance of measurement rates for multiple extended target tracking[C]. 2012 15th International Conference on Information Fusion, Singapore, 2012: 2170–2176. [107] SWAIN A and CLARK D. Extended object filtering using spatial independent cluster processes[C]. 2010 13th International Conference on Information Fusion, Edinburgh, UK, 2010: 1–8. [108] SWAIN A and CLARK D. The PHD filter for extended target tracking with estimable extent shape parameters of varying size[C]. 2012 15th International Conference on Information Fusion, Singapore, 2012: 1111–1118. [109] LIAN Feng, HAN Chongzhao, LIU Weifeng, et al. Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets[J]. Signal Processing, 2012, 92(7): 1729–1744. doi: 10.1016/j.sigpro.2012.01.009 [110] LUNDQUIST C, GRANSTRÖM K, and ORGUNER U. An extended target CPHD filter and a gamma Gaussian inverse Wishart implementation[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 472–483. doi: 10.1109/JSTSP.2013.2245632 [111] BEARD M, REUTER S, GRANSTRÖM K, et al. Multiple extended target tracking with labeled random finite sets[J]. IEEE Transactions on Signal Processing, 2016, 64(7): 1638–1653. doi: 10.1109/TSP.2015.2505683 [112] WILLIAMS J. Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1664–1687. doi: 10.1109/TAES.2015.130550 [113] GARCÍA-FERNÁNDEZ Á F, WILLIAMS J L, GRANSTRÖM K, et al. Poisson multi-Bernoulli mixture filter: Direct derivation and implementation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(4): 1883–1901. doi: 10.1109/TAES.2018.2805153 [114] GRANSTRÖM K, FATEMI M, and SVENSSON L. Poisson multi-Bernoulli mixture conjugate prior for multiple extended target filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(1): 208–225. doi: 10.1109/TAES.2019.2920220 [115] CARMI A, SEPTIER F, and GODSILL S J. The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking[J]. Automatica, 2012, 48(10): 2454–2467. doi: 10.1016/j.automatica.2012.06.086 [116] WIENEKE M and KOCH W. A PMHT approach for extended objects and object groups[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2349–2370. doi: 10.1109/TAES.2012.6237596 [117] 杜浩翠, 谢维信, 范建德. 基于PPP模型的多扩展目标跟踪的JPDA算法研究[J]. 信号处理, 2019, 35(6): 1079–1087. doi: 10.16798/j.issn.1003-0530.2019.06.020DU Haocui, XIE Weixin, and FAN Jiande. JPDA algorithm for multi-extended target tracking based on PPP model[J]. Journal of Signal Processing, 2019, 35(6): 1079–1087. doi: 10.16798/j.issn.1003-0530.2019.06.020 [118] 陈一梅, 刘伟峰, 孔明鑫, 等. 基于GLMB滤波和Gibbs采样的多扩展目标有限混合建模与跟踪算法[J]. 自动化学报, 2020, 46(7): 1445–1456. doi: 10.16383/j.aas.c180077CHEN Yimei, LIU Weifeng, KONG Mingxin, et al. A modeling and tracking algorithm of finite mixture models for multiple extended target based on the GLMB filter and Gibbs sampler[J]. Acta Automatica Sinica, 2020, 46(7): 1445–1456. doi: 10.16383/j.aas.c180077 [119] 胡琪. 基于随机矩阵的扩展目标跟踪算法研究[D]. [博士论文], 西安电子科技大学, 2018.HU Qi. Extended target tracking algorithms based on random matrix[D]. [Ph. D. dissertation], Xidian University, 2018. [120] 樊鹏飞, 李鸿艳. 基于GIW-PHD的扩展目标联合跟踪与分类算法[J]. 电子学报, 2018, 46(7): 1562–1570. doi: 10.3969/j.issn.0372-2112.2018.07.004FAN Pengfei and LI Hongyan. Joint tracking and classification of extended object based on the GIW-PHD filter[J]. Acta Electronica Sinica, 2018, 46(7): 1562–1570. doi: 10.3969/j.issn.0372-2112.2018.07.004 [121] 杜金瑞. 基于多伯努利滤波器的多扩展目标跟踪方法研究[D]. [硕士论文], 兰州理工大学, 2018.DU Jinrui. The research on multi-extended target tracking algorithm based on multi-Bernoulli filter[D]. [Master dissertation], Lanzhou University of Technology, 2018. [122] 李鹏. 基于随机有限集的多扩展目标跟踪和航迹维持算法研究[D]. [博士论文], 江南大学, 2018.LI Peng. Research on multiple extended target tracking and trajectory maintenance algorithms based on random finite set[D]. [Ph. D. dissertation], Jiangnan University, 2018. [123] 孙力帆, 何子述, 冀保峰, 等. 基于高精度传感器量测的机动扩展目标建模与跟踪[J]. 光学学报, 2018, 38(2): 0228001. doi: 10.3788/AOS201838.0228001SUN Lifan, HE Zishu, JI Baofeng, et al. Modeling and tracking of maneuvering extended objects using high resolution sensors[J]. Acta Optica Sinica, 2018, 38(2): 0228001. doi: 10.3788/AOS201838.0228001 [124] 柳超, 孙进平, 陈小龙, 等. 结合幅度信息的扩展目标随机有限集跟踪方法[J]. 雷达学报, 2020, 9(4): 730–738. doi: 10.12000/JR19071LIU Chao, SUN Jinping, CHEN Xiaolong, et al. Random finite set-based extended target tracking method with amplitude information[J]. Journal of Radars, 2020, 9(4): 730–738. doi: 10.12000/JR19071 [125] VIVONE G, BRACA P, and ERRASTI-ALCALA B. Extended target tracking applied to X-band marine radar data[C]. OCEANS 2015, Genoa, Italy, 2015: 1–6. [126] VIVONE G, BRACA P, NATALE A, et al. Converted measurements Bayesian extended target tracking applied to X-band marine radar data[J]. Journal of Advances in Information Fusion, 2017, 12(2): 189–210. [127] VIVONE G and BRACA P. Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 1007–1019. doi: 10.1109/JOE.2015.2503499 [128] MAGNANT C, KEMKEMIAN S, and ZIMMER L. Joint tracking and classification for extended targets in maritime surveillance[C]. 2018 IEEE Radar Conference, Oklahoma City, USA, 2018: 1117–1122. [129] ZHOU Yi, WANG T, HU Ronghua, et al. Multiple kernelized correlation filters (MKCF) for extended object tracking using X-band marine radar data[J]. IEEE Transactions on Signal Processing, 2019, 67(14): 3676–3688. doi: 10.1109/TSP.2019.2917812 [130] LIU Chao, SUN Jinping, and WANG Ziwei. Double-directional Bernoulli track-before-detect filter with particle flow[C]. 2019 International Conference on Control, Automation and Information Sciences, Chengdu, China, 2019: 1–6. [131] 柳超, 孙进平, 袁常顺, 等. Geodesic流多伯努利检测前跟踪方法[J]. 电子学报, 2020, 48(7): 1375–1379. doi: 10.3969/j.issn.0372-2112.2020.07.17LIU Chao, SUN Jinping, YUAN Changshun, et al. Multi-Bernoulli track-before-detect method with Geodesic flow[J]. Acta Electronica Sinica, 2020, 48(7): 1375–1379. doi: 10.3969/j.issn.0372-2112.2020.07.17 -
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