Improved De-Multipath Neural Network Models With Self-Paced Feature-to-Feature Learning for DOA Estimation in Multipath Environment

When the elevation of target is smaller than a beamwidth, the complex multipath signals will distort the feature of direct signal reflected from target. The elevation of target can hardly be estimated accurately. Hence, in this paper, we proposed three kinds of neural networks models including deep neural network (DNN), 1-D convolutional neural network (1-D CNN) and 2-D convolutional neural network (2-D CNN) and their optimization method to mitigate phase distortion caused by multipath signals and enhance the phase feature of direct signal. The direction of arrival (DOA) estimation accuracy of physics-driven methods including digital beamforming (DBF) and multiple signal classification (MUSIC) is effectively improved. Concretely, we analyze the origins of error of DOA estimation in multipath environment and discuss the importance of phase feature to DOA estimation. A complete framework of feature-to-feature phase enhancement is built for DOA estimation in radar systems. The results of experiments with real data collected from a very high frequency (VHF) radar demonstrate the superior DOA estimation performance of proposed feature-to-feature learning methods with respect to other state-of-the-art methods including physics-driven methods and existing data-driven methods.