Residual Neural Network for Direction-of-Arrival Estimation of Multiple Targets in Low SNR

Abstract

In this paper, a novel direction-of-arrival (DOA) estimation method is proposed for linear arrays on the basis of residual neural network (ResNet). The real parts, imaginary parts, and phase entries of the spatial covariance matrix from the on-grid angles are used as the input of ResNet for training, and the angular directions formulated as a multilabel classification task are predicted using the sample covariance matrix from the off-grid angles during the testing phase. ResNet demonstrates robustness in the scenarios on a fixed number of signals and a mixed number of signals. Simulation results show that ResNet can achieve significant performance in DOA estimation compared to multiple signal classification, estimation of signal parameters via rotation invariance techniques, convolutional neural network (CNN), and deep complex-valued CNN in low signal-to-noise ratio.