Sonar echo detection based on two-dimensional matched filtering and convolutional neural network

Abstract

In underwater acoustics, the performance of sonar echo detection is limited when the echo-to-noise ratio or echo-to-reverberation ratio is low. As an attempt to improve the echo detection rate and maintain a low false alarm rate, a method based on two-dimensional matched filtering (2-D-MF) and convolutional neural network (CNN) is proposed. The 2-D-MF divides the replica signal into multiple sub-replicas, each with different frequency components, and utilizes the sub-replicas to perform the matched filtering individually, obtaining 2-D-MF features that better represent the amplitude-frequency characteristics of the echo signal. The CNN is utilized as an echo detector to extract echo information from the 2-D-MF features and determine the presence of an echo. The proposed method is tested using data collected in the South China Sea, 2021. During the experiment, a transducer transmitted linear frequency modulation (LFM) signals, and a transponder, acting as an analog target, forwarded the LFM signals as echoes. The detection results demonstrate that this method can improve the echo detection rate by approximately 7% while maintaining a constant false alarm rate of 1‰.