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In this article, a regression neural network is proposed instead of the commonly used classification neural network to address the direction of arrival (DOA) estimation problem. Unlike the method treating the DOA estimation problem as a classification task, our proposed method utilizes a regression neural network to directly estimate the signal angles by mapping the input data to the high-dimensional features. It is an end-to-end method where the input data are the signal covariance matrix estimated from the received signals by the uniform linear array (ULA). The network output consists of two branches: one branch provides the estimated angles of signals, and the other branch provides the confidences corresponding to angles, indicating the probabilities that signals are incident at the estimated angles. Owing to the dual-branch output of our proposed method, it is adept at accommodating a variable number of sources without the requirement to alter the network structure. Compared to classification neural networks, our proposed method circumvents the issue of quantization errors and has the capability to handle the imbalance of positive and negative samples. Simulation results validate the superior performance of our proposed method in terms of estimation success rate, angular resolution, and estimation accuracy, especially in scenarios with a variable and unknown number of coherent signals.

Adaptive monopulse algorithm always faces low angle measurement performance in multi-interference environments with low signal-to-noise ratio. To clarify the impact factors, we analyze the performance of monopulse ratio estimation and further derive the Cramer-Rao bound (CRB) for angle estimation. Based on the analysis, a constraint-relaxed adaptive monopulse (RCAM) optimization problem is proposed, and the closed-form solution of the problem is obtained by alternative-direction-method-of-multipliers (ADMM) with fast convergence. Numerical simulations have been provided to demonstrate the effectiveness of the proposed algorithm in improving beamforming and angle estimation.