幅度信息辅助的多机动目标跟踪算法  

Amplitude-information aided tracking of multiple maneuvering targets

作　　者：彭华甫[1,2] 黄高明 田威[1] 满欣 PENG Hua-fu;HUANG Gao-ming;TIAN Wei;MAN Xin(College of Electronic Engineering.Naval Univ.of Engineering,Wuhan 430033,China;Unit No.92773 of PLA,Wenzhou 325807,China)

机构地区：[1]海军工程大学电子工程学院,武汉430033 [2]92773部队,浙江温州325807

出　　处：《海军工程大学学报》2020年第2期25-30,共6页Journal of Naval University of Engineering

基　　金：国家自然科学基金资助项目(61501484);中国博士后科学基金资助项目(2017M613370);海军工程大学自主立项基金资助项目(425517K167)。

摘　　要：针对密集杂波下现有的多机动目标跟踪算法计算量大且性能严重衰退的问题,提出了联合幅度信息的多模型标签多伯努利(AI-MM-LMB)滤波器。首先,对目标状态进行扩展,引入幅度信息;然后,建立幅度信息及位置信息的联合量测似然函数;最后,基于MM-LMB滤波器框架,给出新的更新方程。仿真实验结果表明:低杂波下,AI-MM-LM算法同MM-LMB算法跟踪性能相当;高杂波下,AI-MM-LMB算法性能明显优于MM-LMB算法。Aiming at the problem of large computational complexity and severely degraded perfor-mance of existing multiple maneuvering targets tracking algorithms under dense clutter,a multi-model labeled multi-Bernoulli filter with amplitude information is proposed.Firstly,the target state is extended and the amplitude information is introduced;then the joint measurement likelihood function of amplitude information and location information is established;finally a new update equation is gi-ven based on the MM-LMB filter framework.Simulation results show that the tracking performance of AI-MM-LMB algorithm and MM-LMB algorithm is similar under low clutter;while under high clutter,AI-MM-LMB algorithm significantly outperforms MM-LMB algorithm.

关 键 词：多目标跟踪 机动目标 标签随机有限集 幅度信息 

分 类 号：TN953[电子电信—信号与信息处理]