J4 ›› 2009, Vol. 36 ›› Issue (3): 410-417.

• 研究论文 • 上一篇    下一篇

一种新的雷达HRRP自适应划分角域建模方法

陈凤;侯庆禹;刘宏伟;保铮   

  1. (西安电子科技大学 雷达信号处理重点实验室,陕西 西安  710071)
  • 收稿日期:2008-04-25 修回日期:2008-06-25 出版日期:2009-06-20 发布日期:2009-07-04
  • 基金资助:

    “教育部长江学者和创新团队支持计划”资助(IRT0645);国家自然科学基金资助(60772140);国家部委预研项目及国家部委预研基金联合资助

New adaptive angular-sector segmentation algorithm for radar ATR based on HRRP

CHEN Feng;HOU Qing-yu;LIU Hong-wei;BAO Zheng     

  1. (Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an  710071, China)
  • Received:2008-04-25 Revised:2008-06-25 Online:2009-06-20 Published:2009-07-04

摘要:

基于雷达方位渐变高分辨距离像(HRRP)的连续性,提出了一种自适应递归划分角域的建模方法,利用自适应高斯分类器和高斯过程分类器,从雷达数据中提取连续HRRP序列中包含的非线性结构信息;提出了一种判定角域边界的准则,递归地对雷达数据自适应划分角域.实测数据仿真试验证明了该方法优于传统的等间隔划分角域建模法.

关键词: 高分辨距离像, 自动目标识别, 等间隔划分角域, 自适应划分角域, 自适应高斯分类器, 高斯过程分类器

Abstract:

In the field of high resolution range profile (HRRP) based radar automatic target recognition (ATR), it is an effective approach to tackle the target-aspect sensitivity by segmenting continuous radar data into several sectors in equal angular intervals. This equal angular-sector segmentation algorithm is based on the condition for good quality of ISAR images, but it is not a satisfactory choice with the application of ATR for its inclination to cause model mismatch, performance limitation, and time-consumption. This paper proposes a recursive algorithm for adaptively angular sector segmenting based on Adaptive Gaussian Classifier (AGC) and Gaussian Processes classifier (GPC).Since the HRRP data are continuous along the azimuth,we first exploit the nonlinear structure characteristic embedded in HRRP data through AGC or GPC, then present a criterion for determining the angular-sector boundary, and finally recursively segment the data. Promising experimental results are presented for measured radar data.

Key words: high resolution rang profile (HRRP), automatic target recognition (ATR), equal angular-sector segmentation, adaptive angular-sector segmentation, Adaptive Gaussian Classifier (AGC), Gaussian Processes classifier (GPC)

中图分类号: 

  • TN959.1+7