降维STAP中稀疏恢复的角度多普勒通道选择方法

在机载雷达信号处理中，高强度的地杂波严重影响信号检测性能，而空时自适应处理(STAP)是一种有效抑制杂波的技术。实际处理中，由于杂波的非均匀性，空时自适应处理往往面临着可用有效样本数较少的问题，同时机载雷达处理的信号维度极为庞大。为了解决这些问题，提出了一种基于稀疏恢复的降维STAP通道选择方法。利用少量样本通过稀疏恢复的方法估计出全维度的杂波协方差矩阵(CCM)，并以此为依据评估各个通道的重要性，选择合适的通道构造出降维后的杂波协方差矩阵并进行STAP处理，解决了有效样本较少的问题，同时保证了降维算法的性能。数值仿真验证了算法有效且比典型的稀疏STAP算法效果更好，讨论了在不同样本数下，输出性能与通道数的关系，结论具有工程应用意义。

Angle-Doppler Channel Selection Method via Sparse Recovery for Reduced-Dimension STAP

In the airborne radar signal processing, the strong ground clutter is a major problem affecting the signal detection performance. The space-time adaptive processing (STAP) is an effective technique to suppress the clutter. In practical processing, because of the non-stationarity of the clutter, the STAP usually faces the problem of a small number of available valid samples. In order to solve this problem, an angle-Doppler channel selection method based on sparse recovery is proposed. We utilize a small number of samples to estimate the full-dimensional clutter covariance matrix (CCM) via the sparse recovery method and evaluate the importance of each channel with the estimated full-dimensional CCM. Then we select the appropriate channels to construct the reduced-dimensional clutter covariance matrix for the STAP processing. The proposed algorithm can solve the problem of few samples with good performance of the reduced-dimension STAP (RD-STAP). The numerical simulation verifies that the algorithm is effective and better than several typical STAP algorithms. The relationship between output performance and the number of channels under different sample numbers is also discussed.