直接基于目标距离环杂波特性的样本选择
投稿时间:2020-09-27  修订日期:2020-10-27  点此下载全文
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作者单位E-mail
秦琴 上海第二工业大学 qinqin@sspu.edu.cn 
屠子美 上海第二工业大学 zmtu@sspu.edu.cn 
李明 电子科技大学  
基金项目:上海第二工业大学学科基金(XXKZD1603)
中文摘要:为了保障机载雷达空时自适应处理性能,需确保所提供的训练样本与目标距离环(Cell Under Test, CUT)杂波满足独立同分布特性,但在实际场景中训练样本很可能被干扰目标信号污染,破坏其均匀性,导致处理结果出现目标自消现象,严重影响检测性能。而常规样本选择算法采用样本协方差矩阵表征CUT的杂波特性,当大部分样本的杂波特性偏离CUT的杂波特性时,则会导致表征不准确,严重降低筛选效率。因此本文提出了一种基于CUT杂波特性直接估计的机载雷达训练样本选择算法。该方法直接利用CUT的子孔径协方差矩阵对杂波进行表征,由于估计过程不依赖训练样本,因此可不受样本中干扰目标影响。同时考虑目标距离环中可能存在目标信号,因此所提算法采用CUT子孔径协方差矩阵的Capon谱对目标可能存在的区域以外进行积分重构,从而剔除协方差矩阵中的目标成分。相比于传统广义内积算法利用单拍数据计算检验统计量,基于CUT杂波特性直接估计算法以样本的子孔径协方差矩阵表征其特性,可使计算结果更加稳定。通过仿真实验表明本文所提出的算法在筛选训练样本时更加准确。
中文关键词:雷达  非均匀杂波  子孔径  干扰目标信号  协方差矩阵重构
 
Sample selection based on direct estimation of cell under test clutter characteristics
Abstract:In order to guarantee the space-time adaptive processing performance of radar, it is necessary to ensure that training samples should be independent and identically distributed and share the same statistic property with the clutter in the cell under test (CUT). However, in the practical application, the training samples are likely to be contaminated by the inference target signals(outliers), resulting in the so-called target self-nulling phenomenon, and seriously degrading the detection performance. However, the conventional sample selection algorithms use the sample covariance matrix to represent the clutter characteristics of CUT. When the clutter characteristics of most samples deviate from the CUT, the representation is inaccurate, reducing the censoring efficiency. Therefore, this paper proposes a training sample selection algorithm for radar based on direct estimation of the CUT clutter characteristics. The proposed method directly uses the sub-aperture covariance matrix of CUT to characterize the clutter. Since the estimation process depends on no training samples, the estimation of CUT is not affected by the outliers. Moreover, considering the existence of target signal in the CUT, the proposed method removes the target component from the sub-aperture covariance matrix of CUT based on clutter covariance matrix reconstruction, which utilizes the clutter Capon spectrum integrated over a sector separated from the location of target. Compared with the traditional generalized inner product algorithm which uses single snapshot to calculate the detection parameters, the new algorithm uses the sub-aperture covariance matrix of the samples to characterize its statistical characteristics, obtaining more stable results. The simulation results show that the proposed algorithm selects training samples more accurately.
keywords:Radar  heterogeneous clutter  sub-aperture  inference target signals  covariance matrix reconstruction
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