A Deep Learning Approach for Sparse Single Snapshot DOA Estimation

‍ ‍Based on the sparsity of signal， Sparse Recovery （SR） method can use single snapshot data for high-resolution DOA （Direction of Arrival） estimation of correlated signal sources. However， existing methods for solving the SR-DOA model always suffer from the problems of parameter setting difficulty， high computational complexity， or low recovery accuracy， limiting their practical applications. To solve these problems， this paper proposes Smoothed L0 Net （SL0-Net）， which combines the model-based SL0 algorithm and the data-driven deep learning method to solve the SR-DOA model. At first， the SR model for DOA estimation is established and the SL0 algorithm used to solve this model is analyzed. Then， based on deep learning framework， SL0-Net is constructed， whose parameters are trained with sufficient and complete datasets. At last， the trained SL0-Net is used for solving the SR-DOA model， achieving the high-resolution DOA estimation result. Simulation results show that， compared with existing typical algorithms， the proposed SL0-Net is more suitable for fast and high-resolution DOA estimation under the condition of unknown signal source number.