基于海杂波稀疏性与非均匀度的样本挑选方法

针对预警雷达对海监视面临海杂波分布非均匀与杂波样本受目标污染,导致自适应杂波抑制处理性能恶化和目标能量损失的问题,提出了一种基于海杂波稀疏性与非均匀度的样本挑选方法。该方法将目标的导向约束与广义内积样本挑选方法结合,先利用海杂波在空时二维平面上的稀疏分布特性,根据海杂波与目标空时二维分布差异剔除被目标污染的样本,再利用广义内积准则衡量海杂波分布的非均匀程度,并获取均匀样本,以提高杂波协方差矩阵的估计精度。仿真结果表明:所提方法能在提高杂波抑制性能的同时,减小目标信号能量损失。该方法可广泛应用于海面预警监视雷达系统。

Sample Selection Method Based on Sparse Characteristic and Heterogeneous Degree of Sea Clutter

As for the early-warning radars in sea surveillance, the heterogeneous and target-contaminated clutter samples may lead to a poor performance of adaptive clutter suppression processing and target energy loss. To address this problem, a sample selection method is proposed based on the sparse characteristic and heterogeneous degree of sea clutter in this paper, which creatively combines the constraint of the target vector with the generalized inner product (GIP) criterion. Firstly, based on the sparse distribution of sea clutter on two-dimensional space-time plane, the samples contaminated by moving targets are eliminated according to the diversity of space-time distribution between sea clutter and moving targets. Subsequently, the heterogeneous degrees of remaining samples are measured by GIP criterion, and on this basis, the estimation accuracy can be improved on clutter covariance matrix constructed by the obtained homogeneous samples. Finally, the simulation results show that the proposed method can improve the clutter suppression performance as well as reduce the energy loss of the target. The method can be widely applied to the early-warning radars in sea surveillance.