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SGPDA-STF及其在水下多目标纯方位跟踪中的应用
Alternative TitleSGPDA-STF and Its Application of Underwater Multiple Targets Bearings-Only Tracking
刘开周; 梅登峰; 王艳艳
Department水下机器人研究室
Source Publication机械设计与制造
ISSN1001-3997
2014
Issue12Pages:220-223
Contribution Rank1
Funding Organization国家自然科学基金(61273334); 辽宁省自然科学基金(2011010025-401)
Keyword广义概率数据关联 强跟踪 纯方位跟踪 多目标跟踪 杂波
Abstract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。
Other AbstractFor 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 is 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. SGPDASTF 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.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15673
Collection水下机器人研究室
Corresponding Author刘开周
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院大学
Recommended Citation
GB/T 7714
刘开周,梅登峰,王艳艳. SGPDA-STF及其在水下多目标纯方位跟踪中的应用[J]. 机械设计与制造,2014(12):220-223.
APA 刘开周,梅登峰,&王艳艳.(2014).SGPDA-STF及其在水下多目标纯方位跟踪中的应用.机械设计与制造(12),220-223.
MLA 刘开周,et al."SGPDA-STF及其在水下多目标纯方位跟踪中的应用".机械设计与制造 .12(2014):220-223.
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