摘要
对于辐射源目标无源定位,充分利用冗余数据,降低系统误差,提高定位精度和算法普适性在现实工程应用中有着十分重要意义。提出了一种基于数据融合的期望最大化(EM)定位算法,通过对采样数据进行集中融合处理后极大化更新参数集,然后再迭代计算,直到达到阈值设定,最终确定目标估计位置。仿真结果表明,采用EM算法,可以有效利用多种定位方法的冗余采样数据,在强噪声环境下,可以快速有效地提高系统的定位精度,并对多个密集目标实现区分。
For passive positioning of radiation source targets,making full use of redundant data,reducing system errors,improving positioning accuracy and algorithm universality is of great significance in practical engineering applications.This paper proposes an expectation maximization(EM)positioning algorithm based on data fusion,which maximizes and updates the parameter set after intensive fusion processing of the sampled data,and then iterative calculations,until the threshold setting is reached,and finally the target estimated position is determined.The simulation results show that the EM algorithm can effectively utilize the redundant sampling data of multiple positioning methods.In a strong noise environment,the positioning accuracy of the system can be quickly and effectively improved,and multiple dense targets can be distinguished.
作者
陈为业
刘广怡
沈智翔
李鸥
闫东岳
CHEN Weiye;LIU Guangyi;SHEN Zhixiang;LI Ou;YAN Dongyue(Information Engineering University,Zhengzhou 450001, China;Unit 66407, Beijing 100080, China)
出处
《信息工程大学学报》
2021年第4期399-404,共6页
Journal of Information Engineering University
关键词
EM算法
数据融合
多目标定位
EM Algorithm
data fusion
multi-target positioning
