为简化联合概率数据关联算法(Joint Probabilistic Data Association,JPDA)的计算复杂度,增强JPDA算法的实时性,设计了一种新的JPDA简化算法。首先根据目标航迹与量测之间的关联规则,定义了一种新的计算关联概率的方法,之后分析公共量...为简化联合概率数据关联算法(Joint Probabilistic Data Association,JPDA)的计算复杂度,增强JPDA算法的实时性,设计了一种新的JPDA简化算法。首先根据目标航迹与量测之间的关联规则,定义了一种新的计算关联概率的方法,之后分析公共量测对目标的影响,引入公共量测影响因子修正关联概率。该算法不用进行确认矩阵拆分,有效解决了在密集杂波环境下因回波密度增加而造成的计算上的组合爆炸问题。仿真结果表明,简化的JPDA算法能够在保持对目标有效跟踪的情况下,大大缩短计算时间,提高算法的实时性。展开更多
针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)...针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)。首先,采用最大熵模糊聚类求得的隶属度初步表征目标与有效量测之间的关联概率。其次,采用基于目标距离的量测修正因子对关联概率进行调整,并建立关联概率矩阵。最后,结合卡尔曼滤波算法,对目标的状态进行加权更新。仿真结果表明,所提算法在杂波环境下的跟踪性能相比现有的两种关联算法有较大提升,是一种有效的多目标跟踪数据关联算法。展开更多
We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are dis...We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.展开更多
文摘为简化联合概率数据关联算法(Joint Probabilistic Data Association,JPDA)的计算复杂度,增强JPDA算法的实时性,设计了一种新的JPDA简化算法。首先根据目标航迹与量测之间的关联规则,定义了一种新的计算关联概率的方法,之后分析公共量测对目标的影响,引入公共量测影响因子修正关联概率。该算法不用进行确认矩阵拆分,有效解决了在密集杂波环境下因回波密度增加而造成的计算上的组合爆炸问题。仿真结果表明,简化的JPDA算法能够在保持对目标有效跟踪的情况下,大大缩短计算时间,提高算法的实时性。
文摘针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)。首先,采用最大熵模糊聚类求得的隶属度初步表征目标与有效量测之间的关联概率。其次,采用基于目标距离的量测修正因子对关联概率进行调整,并建立关联概率矩阵。最后,结合卡尔曼滤波算法,对目标的状态进行加权更新。仿真结果表明,所提算法在杂波环境下的跟踪性能相比现有的两种关联算法有较大提升,是一种有效的多目标跟踪数据关联算法。
基金Project supported by the National Natural Science Foundationof China(Nos.61174142,61222310,and 61374021)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109)+3 种基金theZhejiang Provincial Science and Technology Planning Projects ofChina(No.2012C21044)the Marine Interdisciplinary ResearchGuiding Funds for Zhejiang University(No.2012HY009B)theFundamental Research Funds for the Central Universities(No.2014XZZX003-12)the Aeronautical Science Foundation ofChina(No.20132076002)
文摘We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.
