Usually, only the Cramer-Rao lower bound （CRLB） of single target is taken into consideration in the state estimate of passive tracking systems. As for the case of multitarget, there are few works done due to its com...Usually, only the Cramer-Rao lower bound （CRLB） of single target is taken into consideration in the state estimate of passive tracking systems. As for the case of multitarget, there are few works done due to its complexity. The recursion formula of the posterior Cramer-Rao lower bound （PCRLB） in multitarget bearings-only tracking with the three kinds of data association is presented. Meanwhile, computer simulation is carried out for data association. The final result shows that the accuracy probability of data association has an important impact on the PCRLB.展开更多
针对杂波环境下的多目标跟踪数据互联问题,该文提出基于全邻模糊聚类的联合概率数据互联算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering,ANFCJPDA)。该算法根据确认区域中量测的分布和点...针对杂波环境下的多目标跟踪数据互联问题,该文提出基于全邻模糊聚类的联合概率数据互联算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering,ANFCJPDA)。该算法根据确认区域中量测的分布和点迹-航迹关联规则构造统计距离,以各目标的预测位置为聚类中心,利用模糊聚类方法,计算相关波门内候选量测与不同目标互联的概率,通过概率加权融合对各目标状态与协方差进行更新。仿真分析表明,与经典的联合概率数据互联算法(Joint Probabilistic Data Association algorithm,JPDA)相比,ANFCJPDA较大程度地改善了算法的实时性,并且跟踪精度与JPDA相当。展开更多
文摘Usually, only the Cramer-Rao lower bound （CRLB） of single target is taken into consideration in the state estimate of passive tracking systems. As for the case of multitarget, there are few works done due to its complexity. The recursion formula of the posterior Cramer-Rao lower bound （PCRLB） in multitarget bearings-only tracking with the three kinds of data association is presented. Meanwhile, computer simulation is carried out for data association. The final result shows that the accuracy probability of data association has an important impact on the PCRLB.
文摘针对杂波环境下的多目标跟踪数据互联问题,该文提出基于全邻模糊聚类的联合概率数据互联算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering,ANFCJPDA)。该算法根据确认区域中量测的分布和点迹-航迹关联规则构造统计距离,以各目标的预测位置为聚类中心,利用模糊聚类方法,计算相关波门内候选量测与不同目标互联的概率,通过概率加权融合对各目标状态与协方差进行更新。仿真分析表明,与经典的联合概率数据互联算法(Joint Probabilistic Data Association algorithm,JPDA)相比,ANFCJPDA较大程度地改善了算法的实时性,并且跟踪精度与JPDA相当。
