| 引用本文: | 张俊根.一种粒子势概率假设密度滤波纯方位多目标跟踪算法[J].控制理论与应用,2020,37(6):1319~1325.[点击复制] |
| ZHANG Jun-gen.A particle cardinalized probability hypothesis density filtering algorithm for bearings-only multi-target tracking[J].Control Theory and Technology,2020,37(6):1319~1325.[点击复制] |
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| 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 1499 全文点击 782 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） |
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| 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. |