摘要
在非均匀环境下,针对传统样本挑选、样本加权等方法由于数据利用率低导致独立同分布训练样本不足的问题,该文提出一种在空时2维谱平面联合距离维逐空-时频点谱估计与滤波的协方差矩阵估计方法。该方法根据杂波和目标在距离-空时2维谱平面的分布特性,逐点频估计待检测单元杂波谱,并采用中值滤波方式消除目标污染对地物杂波谱估计的干扰;最后重构无空时孔径损失的杂波协方差矩阵。仿真结果表明,相比于传统非均匀统计STAP方法,所提的距离-空时2维谱滤波方法能够在样本数不足时有效缓解目标信号污染、离散地形杂波或孤立干扰引起的STAP性能下降问题。
The conventional statistical Space-Time Adaptive Processing （STAP） methods, such as sample selection and sample weighting methods, and so forth, have a very low utilization ratio of sample data, which results in that the problem of training samples lack is more prominent in heterogeneous clutter environment. Thus, in this paper, the space-time spectrum of the clutter Cell Under Test （CUT） is estimated according to the distribution characteristics of the clutter and the moving target in the range and space-time two dimensional spectrum plane. In addition, the median filtering is exploited to avoid the disturbance due to the moving target for the estimation of clutter spectrum. Finally, the reconstruction of clutter covariance matrix without sacrificing space-time aperture and clutter suppression is achieved. The results of the simulated experiments demonstrate that the proposed method can effectively alleviate the STAP performance degradation due to the interference target, discrete terrain clutter or isolation interference, compared with the traditional statistical STAP methods.
出处
《电子与信息学报》
EI
CSCD
北大核心
2017年第5期1036-1043,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61671352
61231017)
国家青年科学基金(61501471)
CAST创新基金~~
关键词
机载雷达
空时自适应处理
空时2维谱
谱估计
协方差矩阵
Airborne radar
Space-Time Adaptive Processing （STAP）
Space-time 2-D spectrum
Spectrum estimation： Covariance matrix
