J4 ›› 2015, Vol. 42 ›› Issue (5): 55-62.doi: 10.3969/j.issn.1001-2400.2015.05.010

• 研究论文 • 上一篇    下一篇

一种采用稀疏表示的快速空时自适应方法

解虎1;冯大政1;虞泓波1;袁明冬1;聂卫科2   

  1. (1. 西安电子科技大学 雷达信号处理国家重点实验室,陕西 西安  710071;
    2. 西北大学 信息科学与技术学院,陕西 西安  710127)
  • 收稿日期:2014-05-21 出版日期:2015-10-20 发布日期:2015-12-03
  • 通讯作者: 解虎
  • 作者简介:解虎(1987-),男,西安电子科技大学博士研究生,E-mail: xiehumor@gmail.com.
  • 基金资助:

    国家自然科学基金资助项目(61271293, 61373177);陕西省自然科学基金资助项目(2013JM8008)

Fast space-time adaptive processing method by using the sparse representation

XIE Hu1;FENG Dazheng1;YU Hongbo1;YUAN Mingdong1;NIE Weike2   

  1. (1. National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an  710071, China;
    2. School of Information and Technology, Northwest University, Xi'an  710127, China)
  • Received:2014-05-21 Online:2015-10-20 Published:2015-12-03
  • Contact: XIE Hu

摘要:

在非均匀杂波环境下,空时自适应处理的关键在于如何利用少量样本准确地估计杂波协方差矩阵.基于稀疏表示的杂波协方差矩阵估计方法,仅利用单个或少量样本即可达到较好的杂波协方差矩阵估计效果,明显地提高了空时自适应算法的收敛速度.该方法利用杂波谱的稀疏性,根据稀疏表示理论估计出杂波功率谱,进而估计出杂波协方差矩阵.然而,采用稀疏表示方法估计所得的杂波谱常出现伪峰,容易造成杂波协方差矩阵估计偏差,故利用杂波谱分布的特殊空时耦合性,采用杂波脊曲线拟合方法剔除杂波谱中的伪峰,有效地提高了杂波协方差矩阵估计精度.另外,这种算法还可以对载机飞行参数(载机速度,偏航角等)进行估计.

关键词: 稀疏表示, 非均匀杂波, 协方差矩阵估计, 机载雷达, 基于先验知识的空时自适应算法, 参数估计

Abstract:

One of the key problems of space-time adaptive processing (STAP) is how to estimate the clutter covariance matrix (CCM) accurately with a small number of samples when the clutter environment is heterogeneous. The CCM estimation methods based on sparse representation (CCM-SR) can achieve a good estimation performance with only one or a few samples, which significantly improves the convergence rate of the STAP. By using the sparsity characteristic of the clutter spectrum, the CCM-SR method estimates the clutter spectrum and yields a good estimation of the CCM. However, there are often many pseudo-peaks in the clutter spectrum estimated by the sparse representation (SR), which will cause a CCM estimation error. By exploiting the special relationship of the clutter ridge curve between space domain and Doppler domain, we can eliminate the pseudo-peaks in the clutter spectrum effectively via fitting the curve of the clutter ridge and improve the estimation accuracy of the CCM. In addition, a byproduct of our method is the estimation of the flying parameters (the velocity of the radar platform, the crab angle and so on). Experimental results show that the proposed method can improve the performance of conventional STAP based on sparse representation (STAP-SR) and obtain a good estimation of the flight parameters.

Key words: sparse representation, heterogeneous clutter, clutter covariance matrix estimation, airborne radar, knowledge-aided STAP (KA-STAP), parameters estimation