A Multibaseline Clutter Suppression Approach Assisted by Online Classification for Spaceborne Distributed MIMO Radar Systems

Spaceborne distributed multi-transmit multi-receive (SD-MIMO) radar has great potential to enhance the capability of ground moving target indication (GMTI), particularly with the increasing of space-time-frequency degrees of freedom. However, GMTI performance is severely limited by complex geographical clutter. To address this issue, we proposed a clutter suppression method assisted by online classification using the range-Doppler-band 3D dataset of SD-MIMO radar. The core points and innovations of our method are summarized as follows. Firstly, we extend Markov random field (MRF) classification to 3D space by integrating a 3D adaptive weighted penalty (3D-AWP) function and a probability distribution model of multi-channel clutter. The 3D-AWP function is constructed using a novel spatial distance metric designed to characterize the interaction potential of image pixels within their 3D neighborhood. This ensures that the MRF smooth factor can be adaptively adjusted in both homogeneous and inhomogeneous clutter regions through different weighting values. The probability distribution model of clutter is established by the K-Wishart distribution, which incorporates amplitude and interferometric phase between multiple spatial channels. Then, under the Bayesian framework, we achieve classification of multiband SAR images by solving the maximum posterior probability problem. Subsequently, the homogeneous and inhomogeneous regions are identified based on classification results. And adaptive clutter suppression processing is performed independently for each sub-region. Finally, the SAR-GMTI experiments are performed utilizing synthetic data and measured data. The results demonstrated an improvement in clutter suppression capability by 2 dB and an enhancement in target oSCNR by 1.5 dB compared to traditional methods.