SGPDA-STF及其在水下多目标纯方位跟踪中的应用  

SGPDA-STF and Its Application of Underwater Multiple Targets Bearings-Only Tracking

作　　者：刘开周[1] 梅登峰 王艳艳[1,2] 

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]中国科学院大学,北京100049

出　　处：《机械设计与制造》2014年第12期220-223,共4页Machinery Design & Manufacture

基　　金：国家自然科学基金(61273334);辽宁省自然科学基金(2011010025-401)

摘　　要：针对广义概率数据关联(GPDA)算法存在计算量大、对系统模型变化鲁棒性差等问题,首先提出了简化广义概率数据关联(SGPDA)算法,然后把SGPDA算法与强跟踪滤波器(STF)算法相结合得到基于简化广义概率数据关联的强跟踪滤波器(SGPDA-STF)。SGPDA在不降低精度的条件下,大大降低了计算量。STF通过实时调节增益矩阵,增强了算法的鲁棒性。SGPDA-STF兼具STF鲁棒性强,SGPDA计算量小的优点。进行了水下杂波环境下单站多目标纯方位跟踪的仿真实验,实验结果证明了SGPDA-STF算法在鲁棒性和计算量方面都优于传统的GPDA-EKF算法。For a range of issues, including large amount of calculation and low robustness to model variation, existing in the generalized probability data association （GPDA） algorithm, a simplified generalized probability data association （SGPDA） algorithm is proposed, and then SGPDA/s combined with strong tracking filter （STF） algorithm to form a novel algorithm named strong tracking filter based on simplified generalized probability data association （SGPDA-STF）. SGPDA greatly reduce calculation without decreasing accuracy. The robustness of STF is strengthened by adjusting the gain matrix. SGPDA- STF algorithm combines strong robustness of STF and low calculation cost of SGPDA. The simulation of single observer multiple targets bearings-only tracking in cluttered underwater environment is conducted, and the results show the superiority of SGPDA-STF over the traditional GPDA-EKF algorithm in robustness and calculation cost.

关 键 词：广义概率数据关联 强跟踪 纯方位跟踪 多目标跟踪 杂波 

分 类 号：TH16[机械工程—机械制造及自动化] TN911.72[电子电信—通信与信息系统]