| 引用本文: | 张俊根,姬红兵.高斯混合粒子Cardinalized概率假设密度滤波被动测角多目标跟踪[J].控制理论与应用,2011,28(1):46~52.[点击复制] |
| ZHANG Jun-gen,JI Hong-bing.Gaussian mixture particle Cardinalized probability hypothesis density based passive bearings-only multi-target tracking[J].Control Theory and Technology,2011,28(1):46~52.[点击复制] |
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| 高斯混合粒子Cardinalized概率假设密度滤波被动测角多目标跟踪 |
| Gaussian mixture particle Cardinalized probability hypothesis density based passive bearings-only multi-target tracking |
| 摘要点击 2695 全文点击 2024 投稿时间：2010-01-01 修订日期：2010-04-25 |
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| DOI编号 10.7641/j.issn.1000-8152.2011.1.CCTA100002 |
| 2011,28(1):46-52 |
| 中文关键词 多目标跟踪 随机集 Cardinalized概率假设密度 被动测角 拟蒙特卡罗 |
| 英文关键词 multi-target tracking random sets Cardinalized probability hypothesis density passive bearings-only Quasi-Monte Carlo |
| 基金项目 国家自然科学基金资助项目(60871074). |
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| 中文摘要 |
| 为解决目标数未知或随时间变化的多目标跟踪问题, 通常将多目标状态和观测数据表示为随机集形式, 通过Cardinalized概率假设密度(CPHD)滤波, 递推计算目标的强度(即概率假设密度, PHD)及目标数的概率分布.然而对于被动测角的非线性跟踪问题, CPHD无法获得闭合解. 为此, 本文提出一种新的高斯混合粒子CPHD算法, 利用高斯混合近似PHD, 避免了用聚类确定目标状态, 同时, 将拟蒙特卡罗(QMC)积分方法引入计算目标状态的预测和更新分布, 取得了良好的效果. |
| 英文摘要 |
| When the number of targets is unknown or varies with time, multi-target state and measurements are expressed as random sets and the multi-target tracking problem is addressed by the Cardinalized probability hypothesis density(CPHD) filter, which propagates not only the probability hypothesis density(PHD) of the joint distribution but also the full probability distribution on target number. However, the CPHD can not provide a closed-form solution to the nonlinear problem occurred in the passive bearings-only multi-target tracking system. A novel Gaussian mixture particle CPHD(GMPCPHD) filter is presented in the paper. The PHD is approximated by a mixture of Gaussians, which avoids clustering in the determination of target states. In addition, Quasi-Monte Carlo integration method is introduced to approximate the prediction and update distributions of target states. Simulation results verify the effectiveness of the proposed GMPCPHD. |