摘要
"边录取、边学习、边建模"是雷达高分辨距离像(high resolution range profile,HRRP)统计识别工程化的方法之一。在独立高斯模型假设下,推导了参数在线学习的公式,根据概率密度值提出一种双门限在线统计识别方法,首先设置门限SA剔除HRRP中的"环值",然后设置门限SB将数据分成几段,从而缓减模型与实时HRRP数据多模特性的失配。基于实测数据的仿真实验证明了本方法的有效性。
A feasible approach to the practical application of radar automatic target recognition （RATR） based on high resolution range profile （HRRP） is enrolling data while learning and modeling interactively and concurrently. On the assumption that echoes in range cells are independent Gaussian distributed, online learning parameters are first deduced, and then a two-threshold method based on probabilistic density fanction is proposed. The threshold SA picks the outliers and the threshold SB reduces the mismatch between the statistics model and the online real HRRP data. Experiments based on measured data show the effectiveness of the proposed method.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第8期1785-1789,1850,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(60772140)
教育部长江学者和创新团队支持计划(IRT0645)
国防预研项目
国防预研基金资助课题
关键词
雷达自动目标识别
高分辨距离像
在线算法
统计识别
radar automatic target recognition
high resolution range profile
online algorithm
statistical recognition
