用自编码器和视觉变换器分析无标签时序信号，提升异常检测与分类能力。
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications
- 结合自编码器与视觉变换器，实现时序信号的无监督特征提取。
- 在心电图、雷达波形等数据上实现高精度异常检测与分类。
- 适合信号智能领域研究者，尤其关注自监督学习与模型可解释性。
无线通信、雷达、生物医学工程及物联网（IoT）等领域中无标签时序数据的快速增长，推动了无监督学习的发展。本文综述了自编码器与视觉变换器在无监督信号分析中的最新进展，重点探讨其架构设计、应用场景及新兴趋势。研究揭示了这些模型在心电图、雷达波形、物联网传感器数据等多种信号类型上的特征提取、异常检测与分类能力。文章强调了混合架构与自监督学习的优势，同时指出在可解释性、可扩展性及领域泛化方面的挑战。通过连接方法创新与实际应用，本工作为构建鲁棒、自适应的信号智能模型提供了路线图。
原文摘要 · Abstract (English)
The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence.
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