This tutorial-style overview article examines the fundamental principles and methods of robustness, using wireless sensing and communication (WSC) as the narrative and exemplifying framework. First, we formalize the conceptual and mathematical foundations of robustness, highlighting the interpretations and relations across robust statistics, optimization, and machine learning. Key techniques, such as robust estimation and testing, distributionally robust optimization, and regularized and adversary training, are investigated. Together, the costs of robustness in system design, for example, the compromised nominal performances and the extra computational burdens, are discussed. Second, we review recent robust signal processing solutions for WSC that address model mismatch, data scarcity, adversarial perturbation, and distributional shift. Specific applications include robust ranging-based localization, modality sensing, channel estimation, receive combining, waveform design, and federated learning. Through this effort, we aim to introduce the classical developments and recent advances in robustness theory to the general signal processing community, exemplifying how robust statistical, optimization, and machine learning approaches can address the uncertainties inherent in WSC systems.

翻译：本教程式综述文章以无线感知与通信（WSC）为叙事和示例框架，探讨鲁棒性的基本原理与方法。首先，我们形式化鲁棒性的概念与数学基础，重点阐述鲁棒统计、优化与机器学习之间的解释与关联。文中研究了关键技术，如鲁棒估计与检验、分布鲁棒优化、正则化与对抗训练等。同时，讨论了系统设计中鲁棒性的代价，例如名义性能的折衷与额外的计算负担。其次，我们回顾了针对模型失配、数据稀缺、对抗扰动和分布偏移的WSC鲁棒信号处理最新解决方案。具体应用包括基于测距的鲁棒定位、模态感知、信道估计、接收合并、波形设计以及联邦学习。通过这项工作，我们旨在向广大信号处理学界介绍鲁棒性理论的经典发展与最新进展，例证鲁棒统计、优化与机器学习方法如何应对WSC系统中固有的不确定性。