相比于传统机载相控阵雷达，机载发射子孔径-多输入多输出（TS-MIMO）雷达的空域自由度成倍扩大，采用空时自适应处理（STAP）时所需训练样本也显著增长，因此性能在实际非均匀杂波环境下急剧下降，导致慢速运动目标无法检测。不同于传统STAP方法，提出了一种基于空时二维稀疏超分辨谱估计的慢速运动目标检测方法。该方法采用稀疏贝叶斯学习算法直接对待检测距离门数据进行空时二维谱估计，然后再基于雷达先验参数将空时二维超分辨谱中主要杂波分量置零，最后在角-多普勒域进行常规恒虚警处理的检测目标。所提方法无需训练样本，因此可显著提升机载TS-MIMO雷达在实际应用中的慢速运动目标检测能力。仿真实验验证了所提方法的有效性。

Compared with traditional airborne phased array radar, the spatial-domain degrees-of-freedom of the airborne Transmit Subaperturing-Multiple Input Multiple Output (TS-MIMO) radar are multiplied, and the training samples required for Space-Time Adaptive Processing (STAP) are also significantly increased, so the performance degrades sharply in the actual non-uniform clutter environment, and slow moving targets cannot be detected. Different from the traditional STAP method, this paper proposes a slow moving target detection method based on two-dimensional space-time sparse super-resolution spectrum estimation. The method uses the sparse Bayesian learning algorithm to directly measure the range cell data for space-time spectrum estimation. Then, the main clutter component in the space-time super-resolution spectrum is set to zero based on the radar prior parameters, and finally the slow target can be detected in the angle-Doppler domain based onconventional constant false-alarm processing. The proposed method does not require training samples, so it can significantly improve the slow moving target detection capability of the onboard TS-MIMO radar in practical applications. Simulation experiments verify the effectiveness of the proposed method.