DOA Estimation of Underwater Acoustic Array Signal Based on Wavelet Transform With Double Branch Convolutional Neural Network

In underwater acoustic communications, a hydrophone array is often used to receive the underwater acoustic signals to improve the gain of the received signal. The direction of arrival (DOA) estimation is a basic task in underwater acoustic array signal processing. In underwater signal transmission, due to the channel complexity, a large signal transmission loss, and heavy noise interference, the received signal is seriously distorted, and the DOA estimation is poor. In order to improve the accuracy of DOA estimation for underwater acoustic array signals, in this work, we propose continuous wavelet transform with convolutional neural network (CWT-CNN) method. We use a linear factor to improve the calculation process of continuous wavelet transform. The characteristics of time-frequency fusion of signals are calculated by constructing time-frequency array model with modified wavelet factors. The fused features are then used to train the proposed double branch CNN. As compared with other DOA algorithms based on neural networks, the proposed algorithm shows a 5%-20% higher accuracy and 0.479(?) -1.172(?) lower RMSE at 7 different values of SNR. In addition, the proposed algorithm is less computationally complex.