TSeq-GAN: a generalized and robust blind source separation framework for AIS signals of unmanned surface vehicles

Autonomous Identification System (AIS) enables unmanned surface vehicles (USVs) to sense their surrounding environment, enhancing safe navigation. However, AIS signals may collide in congested waterways, degrading sensing performance. Conventional statistical blind source separation (BSS) algorithms struggle to isolate signals lacking strictly non-Gaussian features in complex communication environments. Due to Gaussian filtering in AIS signal modulation, essential higher-order statistics are lost, often leading to low accuracy and instability with conventional methods. To this end, this paper develops a time sequence generative adversarial network (TSeq-GAN)-enabled BSS method. The proposed approach replaces an ordered training set with a randomly constructed AIS mixed signal matrix and incorporates a spatialtemporal feature extraction network paired with a generative adversarial framework to capture multidimensional signal characteristics and reconstruct the original signals. Furthermore, a global multi-objective optimization strategy is applied to the loss function to balance error minimization and signal quality. Under a 5 dB signal-to-noise ratio (SNR) and varying numbers of mixed signals, experimental results show that the method reduces mean squared error (MSE) by at least 9.84%, improves signal-to-interference ratio (SIR) by 10.03%, and increases continuous mutual information (cMI) by at least 4.11% compared to existing techniques, validating its robust and accurate extraction of AIS signals.