Following the recent progress in terahertz (THz) signal generation and radiation methods, joint THz communications and sensing (CAS) applications are being proposed for future wireless systems. Toward this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this resurgent interest in THz sensing for efficient utilization of the THz band. In this article, we present an overview of these techniques, with an emphasis on signal preprocessing [standard normal variate (SNV) normalization, minimum–maximum normalization, and Savitzky–Golay (SG) filtering], feature extraction [principal component analysis (PCA), partial least squares (PLS), t-distributed stochastic neighbor embedding (t-SNE), and nonnegative matrix factorization (NMF)], and classification techniques [support vector machines (SVMs), the k-nearest neighbor (kNN), discriminant analysis (DA), and naive Bayes (NB)]. We also address the effectiveness of deep learning techniques by exploring their promising sensing and localization capabilities at the THz band. Finally, we investigate the performance and complexity tradeoffs of the studied methods in the context of joint CAS (JCAS). We thereby motivate corresponding use cases and present a handful of contextual future research directions.

中文翻译：

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太赫兹传感的信号处理和机器学习技术：概述


随着太赫兹（THz）信号生成和辐射方法的最新进展，人们正在为未来的无线系统提出联合太赫兹通信和传感（CAS）应用。为此，太赫兹光谱预计将被应用到用户设备上，以识别感兴趣的材料和气体成分。太赫兹特定的信号处理技术应该补充人们对太赫兹传感的重新兴起的兴趣，以有效利用太赫兹频段。在本文中，我们概述了这些技术，重点是信号预处理 [标准正态变量 (SNV) 归一化、最小-最大归一化和 Savitzky-Golay (SG) 滤波]、特征提取 [主成分分析 (PCA) ）、偏最小二乘法 (PLS)、t 分布随机邻域嵌入 (t-SNE) 和非负矩阵分解 (NMF)] 以及分类技术 [支持向量机 (SVM)、k 最近邻 (kNN)、判别分析（DA）和朴素贝叶斯（NB）]。我们还通过探索深度学习技术在太赫兹频段有前途的传感和定位能力来解决深度学习技术的有效性。最后，我们研究了联合 CAS (JCAS) 背景下所研究方法的性能和复杂性权衡。因此，我们激发了相应的用例，并提出了一些相关的未来研究方向。