KR product and sparse prior based CNN estimator for 2-D DOA estimation

Abstract This paper proposes a method based on Khatri-Rao (KR) product, sparse prior, and convolutional neural networks (CNN) to solve the direction-of-arrival (DOA) estimation problem. Firstly, we use the KR product to expand the degree of freedom (DOF) of the 2-D antenna array. Then we calculate the sparse power spectrum of signals and obtain an RGB image tensor of the spectrum. Finally, we design a CNN group with three different sub-networks to estimate 2-D DOA information. Two of the sub-networks are used for obtaining the spectrum of azimuth angle and elevation angle, respectively. One specific network is designed as the pairing network used for paring azimuth angle with the correct elevation angle. The proposed CNN group is data-driven and does not rely on any prior knowledge of incidence signals. We investigate the feature of estimation error, the root mean squared error (RMSE) responses under different experiment environments, the resolution of the proposed estimation CNN group, and the pairing performance of the proposed pairing network. Comparing with prior estimation methods, the proposed CNN group shows satisfactory estimation accuracy and stability.