一种粒子势概率假设密度滤波纯方位多目标跟踪算法
A particle cardinalized probability hypothesis density filtering algorithm for bearings-only multi-target tracking
摘要点击 52  全文点击 38  投稿时间:2019-06-10  修订日期:2019-11-28
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DOI编号  10.7641/CTA.2019.90437
  2020,37(6):1319-1325
中文关键词  纯方位多目标跟踪  势概率假设密度  粒子滤波  多传感器  均值漂移
英文关键词  bearings-only multi-target tracking  cardinalized probability hypothesis density  particle filter  multisensor  mean-shift
基金项目  学校一般科研项目(2019XYZDX04)
作者单位E-mail
张俊根 北方民族大学电气信息工程学院 zhang_jungen@sina.com 
中文摘要
      针对基于势概率假设密度算法(Cardinalized Probability Hypothesis Density,CPHD)的纯方位多目标跟踪,提出一种新型的多传感器粒子CPHD滤波算法。该算法通过分析混合线性/非线性状态模型的结构信息,结合粒子滤波(PF)与卡尔曼滤波(KF)对各个目标的状态进行预测与估计,运用Mean-Shift方法提取概率假设密度的峰值作为目标状态估计值,并对算法复杂度进行了分析。仿真结果表明,算法可改善目标跟踪效果。
英文摘要
      Aiming at bearings-only multi-target tracking based on cardinalized probability hypothesis density (CPHD) filter and Kalman filter to predict and estimate the states of multiple targets to enhance the estimating performance of the PHD and cardinality distribution. The target state estimates are extracted by utilizing the kernel density estimation theory and mean-shift method. In addition, the complexity of the algorithm is analyzed. Simulation results are presented to demonstrate the improved performance of the proposed filtering algorithms.