Lightweight CNNs-Based Interleaved Sparse Array Design of Phased-MIMO Radar

Conventional transmit subarray partitioning schemes of phased-MIMO radar will cause several problems, the reduction of array aperture, the increase of feeding network complexity and optimization algorithms time cost. Focus on the problems above, this paper proposes a deep learning-based interleaved sparse transmit subarray partitioning method. Firstly, the training data is generated by phased-MIMO array manifold matrix. Secondly, a dimensionality reduction method for radar data is introduced to reduce the dimensionality of the sample data while minimizing information loss. Then, a lightweight convolutional neural network is constructed for training and the optimal array structures is selected by classification. Finally, linear and plane arrays experiment results show that our proposed method can achieve 97.95% classification accuracy, better than other conventional dimensionality reduction methods and neural networks; compared with the traditional SCP partitioning method, our proposed method has similar beampattern sidelobe level and DOA estimation accuracy, but the time cost is greatly reduced.