| 箱粒子PHD演化网络群目标跟踪算法 |
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| 引用本文： | 宋骊平,刘宇航,程轩.箱粒子PHD演化网络群目标跟踪算法[J].控制与决策,2018,33(1):74-80. |
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| 作者姓名： | 宋骊平 刘宇航 程轩 |
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| 作者单位： | 西安电子科技大学电子工程学院，西安710071,西安电子科技大学电子工程学院，西安710071,西安电子科技大学电子工程学院，西安710071 |
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| 基金项目： | 国家自然科学基金项目(61372003)；国家自然科学基金青年项目(61301289). |
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| 摘 要： | 群演化网络模型对群结构的构建和实时更新提供了良好的实现方式.针对粒子概率假设密度(SMC- PHD)滤波算法存在运算量大的问题,提出一种基于箱粒子概率假设密度(BP-PHD)滤波的演化网络群目标跟踪算法.将群演化网络模型得到的群结构信息反馈回BP-PHD滤波过程中,从而实现群目标的跟踪和群数目的估计.对比实验表明,所提出算法可以在保证跟踪效果的同时减少计算量,并且在杂波密集的条件下具有更好的跟踪精度和鲁棒性.
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| 关 键 词： | 演化网络模型 群目标跟踪 箱粒子滤波 概率假设密度 |
Box-particle evolution network PHD filter for group targets tracking |
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| SONG Li-ping,LIU Yu-hang and CHENG Xuan.Box-particle evolution network PHD filter for group targets tracking[J].Control and Decision,2018,33(1):74-80. |
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| Authors: | SONG Li-ping LIU Yu-hang and CHENG Xuan |
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| Affiliation: | School of Electronic Engineering,Xidian University,Xián 710071,China,School of Electronic Engineering,Xidian University,Xián 710071,China and School of Electronic Engineering,Xidian University,Xián 710071,China |
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| Abstract: | The evolving network model provides a better method to deal with the structure of groups, including the formation and real-time update. The box-particle evolution networks probability hypothesis density(PHD) filter for group targets tracking is proposed to improve the increase of computational effort about a sequential Monte Carlo(SMC) PHD filter. The proposed algorithm obtains information of group targets which is combined with BP-PHD and evolving network models, and then feeds back the information to the filter. Consequently, the algorithm realizes the tracking and number estimation of group targets. Comparative experiments show that, the proposed algorithm is more effictive than the SMC-PHD filter, especially in high clutter environments. |
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