摘要
复杂环境下雷达数据关联算法是多目标跟踪领域研究的重难点问题之一。其中,最近邻域算法虽然是一种计算量小、工程易应用的有效数据关联算法,但是存在数据关联正确率不高,滤波结果不够精确和多目标跟踪时易产生错误关联的问题。为改善该算法的数据关联效果,提出了一种最近邻域数据关联算法,通过进一步深度挖掘已知量测信息的熵,按照熵权法分析并确定各自量测指标的权值,再利用权值对最近邻域算法的统计距离关联准则进行优化,从而改善原算法在单目标跟踪中存在的问题。通过仿真实验结果分析得出,该算法相比于原算法具有更高的数据关联正确率、更小的跟踪误差和更快的收敛效果。
The radar data association algorithm in the complex environment is one of the most difficult problems in the field of multi-target tracking.Among them,the nearest neighbor algorithm is an effective data association algorithm with small computation and easy application.However,there is a problem that the data association accuracy is not high,the filtering result is not accurate enough,and the multi-target tracking is easy to cause error correlation.In order to improve the data association effect of the algorithm,a nearest neighbor data association algorithm is proposed.By further deepening the entropy of known measurement information,the entropy weight method is used to analyze and determine the weights of the respective measurement indicators.The weight is optimized for the statistical distance association criterion of the nearest neighbor algorithm,thereby improving the problem of the original algorithm in single target tracking.The simulation results show that the improved algorithm proposed has higher data association accuracy,smaller tracking error and faster convergence than the original algorithm.
作者
李恒璐
陈伯孝
丁一
张钊铭
LI Henglu;CHEN Baixiao;DING Yi;ZHANG Zhaoming(National Laboratory of Radar Signal Processing,Xidian University,Xi'an 710071,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2020年第4期806-812,共7页
Systems Engineering and Electronics
基金
国家自然科学基金（61971323）资助课题
关键词
数据关联
信息熵
最近邻域
熵权法
统计距离
data association
information entropy
nearest neighbor
entropy weight
statistical distance
