基于稀疏贝叶斯学习的机载双基雷达杂波抑制

机载双基雷达杂波与构型有关且具有严重的距离依赖性，因此杂波脊复杂多变，独立同分布(IID)的样本很少。传统的空时自适应处理(STAP)方法受独立同分布样本数的限制，对机载双基雷达杂波的抑制性能有限。基于机载雷达杂波在角度-多普勒域分布的稀疏特性和稀疏贝叶斯学习(SBL)在稀疏信号重建方面的优势，该文将SBL算法应用于较为复杂的机载双基雷达双动模式下杂波抑制，该方法可以用少量训练单元杂波估计待测距离单元的杂波协方差矩阵(CCM)，然后进行空时自适应处理；同时，该算法不需要样本独立同分布，在双基双动模式下对杂波的抑制性能较好，仿真结果验证了算法的有效性。

Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning

Clutter of airborne bistatic radar is related to configuration and has serious range dependence characteristic, therefore the clutter ridge is complex and variable, and few Independent and Identically Distributed (IID) samples exist. As the result, the traditional Space-Time Adaptive Processing (STAP) has a degraded suppression performance for airborne bistatic radar clutter. Based on the sparsity of airborne radar clutter in the angle-Doppler domain and the advantages of Sparse Bayesian Learning (SBL) in sparse signal reconstruction, SBL algorithm is applied to the more complex airborne bistatic radar with both transmitter and receiver moving. The method can estimate the Clutter Covariance Matrix (CCM) of the unit under test with very few training samples, then perform space-time adaptive processing. Since the method does not need independent and identically distributed samples, it has better performance of clutter suppression in the airborne bistatic radar with both transmitter and receiver moving. Simulation results verify the effectiveness of the algorithm.