摘要
为了精确估计概率假设密度(PHD)滤波器的目标数量,提出了一种基于信息加权共识滤波器(ICF)并使用数据聚类的基数补偿方法,将ICF用于信息融合。在密集杂波环境中,当量测中的噪声和杂波较高时,会出现跟踪丢失的情况,进而目标数量估计性能下降。因此,在PHD滤波器中加入基数补偿过程,基于信息融合步骤,使用从PHD滤波器中获得的估计基数和通过数据聚类获得的量测基数,得到最终的目标数量估计。为了验证所提方法的性能,进行了仿真模拟,证明了多目标的跟踪性能得到改善。
In order to accurately estimate the target number of Probability Hypothesis Density(PHD) filters,a cardinality compensation method based on Information-weighted Consensus Filter(ICF) and data clustering is proposed,and ICF is used for information fusion.In a dense clutter environment,when the noise and clutter in the measurement are high,tracking loss will occur,and the performance of target number estimation will be degraded.For this reason,a cardinality compensation process is added to the PHD filter,based on an information fusion step,the estimated cardinality obtained from the PHD filter and the measured cardinality obtained through data clustering are used to obtain the final target number estimation.In order to verify the performance of the proposed method,the simulation is carried out and it is demonstrated that the tracking performance of multiple targets is improved.
作者
王奎武
张秦
WANG Kuiwu;ZHANG Qin(Air Force Engineering,University Air and Missile Defense College,Xi'an 710000 China;Air Force Engineering,University Graduate School,Xi'an 710000 China)
出处
《电光与控制》
CSCD
北大核心
2022年第9期11-16,共6页
Electronics Optics & Control
基金
陕西省自然科学基础研究计划(2022JQ-679)。
关键词
多目标跟踪
信息融合
PHD滤波器
数据聚类
基数补偿
multi-target tracking
information fusion
PHD filter
data clustering
cardinality compensation
