ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1635614
This article is part of the Research TopicBig Data and AI for Sustainable Maritime OperationsView all 5 articles
TSeq-GAN: A Generalized and Robust Blind Source Separation Framework for AIS Signals of Unmanned Surface Vehicles
Provisionally accepted- 1Shanghai Maritime University, pudong, China
- 2Shanghai Ship and Shipping Research Institute CO.LTD, Shanghai, China
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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.
Keywords: Unmanned surface vehicle, automatic identification system, blind source separation, deep learning, Neural Network
Received: 27 May 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Zhao, Peng, Han, Mei, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: You Peng, Shanghai Maritime University, pudong, China
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