为了提高杂波环境下机动目标跟踪的实时性和精确性，在概率数据关联算法的基础上，引入距离加权的概念，以区分来自于目标的量测概率和来自于杂波的虚假概率，在一定程度上提高了概率数据关联算法在密集杂波环境下的非机动目标的跟踪性能.针对机动目标的跟踪，提出了一种适用于密集杂波环境下的联合交互式多模型概率数据关联跟踪算法,该算法利用距离加权的概率数据关联算法进行滤波.模拟实验结果表明：该算法可以在一定程度上提高密集杂波环境下机动目标跟踪的性能，能够更加有效、可靠地实现机动目标跟踪的目的.
In order to improve the accuracy of real-time tracking a maneuvering target in clutter, an improved probabilistic data association was proposed based on the weighted distance and using combined interactive multi-model probabilistic data association algorithm. The algorithm can enhance the probability from the target or decrease the probability from the clutter. The improved algorithm has better tracking performance in the dense clutter environment. The improved probabilistic data association algorithm is used as a filter. Simulation results show that the proposed algorithm can improve maneuvering target tracking performance in dense clutter, can also be more effective and reliable to realize maneuvering target tracking.
［1］MALLICK M, VO B N, KIRUBARAJAN T, et al. Introduction to the issue on multitarget tracking［J］. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 373-375.
［2］何友, 黄勇, 关键, 等. 海杂波中的雷达目标检测技术综述［J］. 现代雷达, 2014, 36(12): 1-9.
HE You, HUANG Yong, GUAN Jian, et al. An overview on radar target detection in sea clutter［J］. Modern Radar, 2014, 36 (12): 1-9.
［3］NADARAJAH N, THARMARASA R, MCDONALD M, et al. IMM forward filtering and backward smoothing for maneuvering target tracking［J］. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3), 2673-2678.
［4］KSZ K, CEMGIL A T. The comparison of the performances of global nearest neighbor and probability hypothesis density filter for varying clutter rates［C］∥2016 24th Signal Processing and Communication Application Conference (SIU). Zonguldak: IEEE Press, 2016: 677-680.
［5］ZENG T, ZHENG L, LI Y, et al. Offline performance prediction of PDAF with Bayesian detection for tracking in clutter［J］. IEEE Transactions on Signal Processing, 2013, 61(3): 770-781.
［6］HABTEMARIAM B, THARMARASA R, THAYAPARAN T, et al. A multiple-detection joint probabilistic data association filter［J］. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 461-471.
［7］HERNANDEZ M, BENAVOLI A. GRAZIANO A, et al. Performance measures and MHT for tracking move-stop-move targets with MTI sensors［J］. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 666-1025.
［8］MAHLER R. Multi-target Bayes filtering via first-order multi-target moments［J］. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.
［9］KIRUBARAJAN T, BAR-SHALOM Y, BLAIR W D, et al. IMMPDAF for radar management and tracking benchmark with ECM［J］.IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4): 1115-1134.
［10］潘泉, 刘刚, 戴冠中, 等. 联合交互式多模型概率数据关联算法［J］. 航空学报, 1999, 20(3): 234-238.
PAN Quan, LIU Gang, DAI Guanzhong, et al, Combined interacting multiple models probabilistic data association algorithm［J］. Acta Aeronautica et Astronautica Sinica, 1999, 20(3): 234-238.
［11］NI L Q, GAO S S, XUE L. Improved probabilistic data association and its application for target tracking in clutter［C］∥2011 International Conference on Electronics, Communications and Control. Ningbo: IEEE Press, 2011: 293-296.
