A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood d...A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.展开更多
Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is ...Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
针对概率数据互联(Probability data association, PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近...针对概率数据互联(Probability data association, PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近邻思想筛选目标量测点,接着利用卡尔曼滤波(Kalman filter, KF)算法实现杂波环境下的快速滤波更新。在此基础上,通过自适应区间平滑方法,动态修正平滑区间,实现整体状态估计的反向平滑,从而提升算法的精度。不同杂波环境下的实验结果表明,本文方法相较于PDA算法与KF-PDA算法,在保证跟踪效率的同时,有效提升了系统状态的估计精度,验证了该方法的鲁棒性和有效性。展开更多
中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,...中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,因此提出一种基于动力学守恒定律的弹道目标概率数据关联(probability data association,PDA)方法,即在传统关联门筛选出有效量测的基础上,对动量矩和机械能进行联合统计检验,进一步剔除电假目标点迹或其他错误量测,并使用动量矩和机械能对加权关联概率进行修正。蒙特卡罗仿真验证了该方法的有效性。仿真结果表明,与传统PDA方法相比,所提方法能够有效抑制有源距离欺骗干扰和杂波的影响,提高跟踪精度。展开更多
基金This project was supported by the National Natural Science Foundation of China (60272024).
文摘A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.
基金the National Natural Science Foundation of China(61771367)the Science and Technology on Communication Networks Laboratory(HHS19641X003).
文摘Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.
文摘中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,因此提出一种基于动力学守恒定律的弹道目标概率数据关联(probability data association,PDA)方法,即在传统关联门筛选出有效量测的基础上,对动量矩和机械能进行联合统计检验,进一步剔除电假目标点迹或其他错误量测,并使用动量矩和机械能对加权关联概率进行修正。蒙特卡罗仿真验证了该方法的有效性。仿真结果表明,与传统PDA方法相比,所提方法能够有效抑制有源距离欺骗干扰和杂波的影响,提高跟踪精度。
