摘要
针对多目标跟踪中概率假设密度(PHD)滤波器在杂波模型与先验知识不匹配情况下滤波性能急剧下降的缺点,将增广状态空间引入PHD滤波器,提出了一种新的未知杂波环境下的PHD滤波器.该滤波器利用增广状态空间区分目标状态空间与杂波状态空间,通过量测对杂波模型进行估计,不需要杂波先验知识,避免了因杂波强度的先验知识选择不当而造成PHD滤波器跟踪性能下降的问题.仿真结果表明,该算法在未知杂波环境下,具有稳定的跟踪效果;在保证实时性的前提下,其跟踪精度与传统PHD滤波器在杂波模型匹配情况下相当.
Aiming at improving the poor performance of the Probability Hypothesis Density（PHD） filter when the clutter model and the prior knowledge are mismatched,a novel PHD filter into which we introduce the augmented state space and which is used under the unknown clutter circumstance is proposed in this paper.The proposed filter can distinguish the target state space and the clutter state space by the augmented state space.Using the estimate of the unknown clutter model from the measurement,the filter can avoid the tracking performance reduction caused by the improper model selection of the unknown clutter.Simulation results show that the proposed algorithm can achieve a stable tracking performance under the unknown clutter circumstance and a tracking accuracy equal to that of the conventional PHD filter used in the unknown clutter circumstance in the real-time context.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2014年第5期18-23,共6页
Journal of Xidian University
基金
国家自然科学基金资助项目(61372003)
国家自然科学基金青年基金资助项目(61101246
61301289)
中央高校基本科研业务费专项资金资助项目(K5051202014)
国家留学基金资助项目(201206965015)
关键词
多目标跟踪
概率假设密度
未知杂波
增广状态空间
multitarget tracking
probability hypothesis density
unknown clutter
augmented state space
