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在机载/星载雷达系统中,空时自适应处理(STAP)可有效抑制杂波并实现动目标检测。基于统计的STAP算法通过平稳的训练样本来估计检测单元内的杂波协方差矩阵,并设计相应的滤波器以提高检测单元的输出信杂比。但训练样本的平稳性在实际快变的杂波环境中无法保证,因而此类算法在实际非均匀杂波环境中性能较差。该文通过挖掘检测单元数据在角度-Doppler域上的稀疏性,提出一种新的直接数据域STAP算法。该算法通过稀疏恢复来获得检测单元的高分辨空时谱估计,有效地避免杂波旁瓣对目标检测的影响,进而实现不经过杂波抑制而直接运动目标检测的目的。同时由于不使用训练样本,可很好地避免训练样本内的非均匀性,该算法在实际非均匀杂波场景中有更广泛的应用前景。
In airborne / spaceborne radar systems, space-time adaptive processing (STAP) can effectively suppress clutter and achieve moving target detection. The statistical-based STAP algorithm estimates the clutter covariance matrix in the detection unit by a stationary training sample and designs the corresponding filter to improve the output signal-to-noise ratio of the detection unit. However, the smoothness of the training samples can not be guaranteed in the fast changing clutter environment. Therefore, the performance of these algorithms is poor in the actual non-uniform clutter environment. In this paper, we propose a new direct data domain STAP algorithm by mining the sparsity of detection unit data over the angle-Doppler domain. The algorithm obtains the high-resolution space-time spectrum of the detection unit by sparse recovery and effectively avoids the influence of the sidelobe of the clutter on the target detection, so as to achieve the purpose of detecting the target directly without clutter suppression. At the same time, the nonuniformity in the training samples can be well avoided because the training samples are not used. This algorithm has a wider application prospect in the actual non-uniform clutter scenarios.