Abstract: To improve the detection performance of multiple access sonar in shallow water acoustic channels, a set of multiple access sonar detection waveforms and their enhanced detection method are proposed. The shallow water target echo channel is modeled and the multiple access sonar data are generated. The WGAN-FG enhanced signal detector is designed, which consists of a generate adversarial network (GAN) signal enhancer and a fully connected convolutional neural network (CNN) classifier, using the fusion gradient (FG) training method. The detection performances of shallow water multiple access sonar echoes are analyzed by simulation methods using the WGAN-FG enhanced signal detector and the traditional detectors such as the CNN, recurrent neural network (RNN), GAN and replica correlation (RC). Simulation results show that the deep learning based neural network detectors have better multi-path, Doppler and mutual interference suppression capabilities than the RC detector. The neural network detectors also have the ability to measure the target speed. And the WGAN-FG enhanced signal detector has better detection performance and target speed identification ability than the other neural network detectors under strong interference or distortion conditions.