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Machine Learning for Radar and Communication Signal Processing

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 16372

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Special Issue Editors

Prof. Dr. Lin Li

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Guest Editor

School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: non-stationary signal processing; intelligent electromagnetic spectrum sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Chuanjin Dai

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Guest Editor

Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
Interests: signal processing; autonomous navigation; brain-inspired artificial intelligence

Dr. Rui Guo

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Guest Editor

School of Automation, Northwestern Polytechnical Universtiy, Xi’an 710129, China
Interests: radar signal processing; radar image processing; radar point cloud processing; radar and laser in remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning approaches, which are commonly applied in computer science disciplines, are beginning to emerge in signal processing for the fields of radar, communications and electronic countermeasures. In contrast to the image and text processing fields, various challenges have arisen in the application of machine learning to electromagnetic signals, such as sensitivity to electromagnetic environments (noises, multipath, interferences, etc.), the presence of multidomain features (time, frequency, time-frequency, cyclic spectrum, spatial spectrum, higher-order statistics, etc.), difficulty in feature understanding, model establishment, multiple scales, generalization and algorithm validation, to name a few.

This Special Issue is aimed at addressing issues in state-of-the-art machine learning approaches applicable in radar, communications and electronic countermeasures domains, providing cross-disciplinary ideas to address present and future challenges. Topics of interest include, but are not limited to:

Blind channel and signal characterization;
Source separation;
Signal recognition;
Automatic modulation classification;
Spectrum sensing;
Positioning and navigation;
Cognitive radio communications;
Radar image processing;
Autonomous navigation.

Prof. Dr. Lin Li
Dr. Chuanjin Dai
Dr. Rui Guo
Guest Editors

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Keywords

machine learning
deep learning
artificial intelligence
signal processing
modulation classification
positioning and navigation
signal recognition
sparse modeling
feature extraction
radar and communication signal processing
remote sensing

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Published Papers (10 papers)

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Research

18 pages, 3406 KiB

Open AccessArticle

Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network

by Yuxuan He, Kunda Wang, Qicheng Song, Huixin Li and Bozhi Zhang

Electronics 2024, 13(18), 3703; https://doi.org/10.3390/electronics13183703 - 18 Sep 2024

Viewed by 832

Abstract

Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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19 pages, 3536 KiB

Open AccessArticle

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

by Dan Yang, Shuya Yang, Junsuo Qu and Ke Wang

Electronics 2024, 13(14), 2707; https://doi.org/10.3390/electronics13142707 - 10 Jul 2024

Viewed by 849

Abstract

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10⁻³, 3.03 × 10⁻³, and 30.66 × 10⁻³, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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12 pages, 9008 KiB

Open AccessArticle

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

by Debjyoti Chowdhury, Nikhitha Vikram Melige, Biplab Pal and Aryya Gangopadhyay

Electronics 2023, 12(24), 5030; https://doi.org/10.3390/electronics12245030 - 16 Dec 2023

Viewed by 1287

Abstract

This paper introduces a computationally inexpensive technique for moving target detection in challenging outdoor environments using millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radars leveraging traditional signal processing methodologies. Conventional learning-based techniques for moving target detection suffer when there are variations in environmental conditions. Hence, the work described here leverages robust digital signal processing (DSP) methods, including wavelet transform, FIR filtering, and peak detection, to efficiently address variations in reflective data. The evaluation of this method is conducted in an outdoor environment, which includes obstructions like woods and trees, producing an accuracy score of 92.0% and precision of 91.5%. Notably, this approach outperforms deep learning methods when it comes to operating in changing environments that project extreme data variations. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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13 pages, 3598 KiB

Open AccessArticle

Prototypical Network with Residual Attention for Modulation Classification of Wireless Communication Signals

by Bo Zang, Xiaopeng Gou, Zhigang Zhu, Lulan Long and Haotian Zhang

Electronics 2023, 12(24), 5005; https://doi.org/10.3390/electronics12245005 - 14 Dec 2023

Cited by 1 | Viewed by 945

Abstract

Automatic modulation classification (AMC) based on data-driven deep learning (DL) can achieve excellent classification performance. However, in the field of electronic countermeasures, it is difficult to extract salient features from wireless communication signals under scarce samples. Aiming at the problem of modulation classification under scarce samples, this paper proposes a few-shot learning method using prototypical network (PN) with residual attention (RA), namely PNRA, to achieve the AMC. Firstly, the RA is utilized to extract the feature vector of wireless communication signals. Subsequently, the feature vector is mapped to a new feature space. Finally, the PN is utilized to measure the Euclidean distance between the feature vector of the query point and each prototype in this space, determining the type of the signals. In comparison to mainstream few-shot learning (FSL) methods, the proposed PNRA can achieve effective and robust AMC under the data-hungry condition. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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16 pages, 6144 KiB

Open AccessArticle

LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

by Feitao Ren, Daying Quan, Lai Shen, Xiaofeng Wang, Dongping Zhang and Hengliang Liu

Electronics 2023, 12(24), 4934; https://doi.org/10.3390/electronics12244934 - 8 Dec 2023

Cited by 1 | Viewed by 1461

Abstract

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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12 pages, 26648 KiB

Open AccessArticle

An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism

by Zhao Ma, Shengliang Fang, Youchen Fan, Gaoxing Li and Haojie Hu

Electronics 2023, 12(17), 3661; https://doi.org/10.3390/electronics12173661 - 30 Aug 2023

Cited by 2 | Viewed by 1456

Abstract

This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branches to obtain a multi-domain mixed feature map. Then, the deep features of the signal are extracted by connecting multiple convolution layers in the time domain. Secondly, a plug-and-play channel attention module is constructed, which can be embedded into any feature extraction layer to give higher weight to the more valuable channels in the output feature map to achieve the purpose of feature correction for the output feature map. The experimental results on the RadiomL2016.10A dataset show that the designed HFECNET-CA has higher recognition accuracy and fewer trainable parameters compared to other networks. Under 20 SNRs, the average recognition accuracy reached 63.92%, and the highest recognition accuracy reached 93.64%. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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10 pages, 1361 KiB

Open AccessCommunication

Self-Location Based on Grid-like Representations for Artificial Agents

by Chuanjin Dai and Lijin Xie

Electronics 2023, 12(12), 2735; https://doi.org/10.3390/electronics12122735 - 19 Jun 2023

Viewed by 1111

Abstract

Self-location plays a crucial role in a framework of autonomous navigation, especially in a GNSS/radio-denied environment. At the current time, self-location for artificial agents still has to resort to the visual and laser technologies in the framework of deep neural networks, which cannot model the environments effectively, especially in some dynamic and complex scenes. Instead, researchers have attempted to transplant the navigation principle of mammals into artificial intelligence (AI) fields. As a kind of mammalian neuron, the grid cells are believed to provide a context-independent spatial metric and update the representation of self-location. By exploiting the mechanism of grid cells, we adopt the oscillatory interference model for location encoding. Furthermore, in the process of location decoding, the capacity of autonomous navigation is extended to a significantly wide range without the phase ambiguity, based on a multi-scale periodic representation mechanism supported by a step-wise phase unwrapping algorithm. Compared with the previous methods, the proposed grid-like self-location can achieve a much wider spatial range without the limitation imposed by the spatial scales of grid cells. It is also able to suppress the phase noise efficiently. The proposed method is validated by simulation results. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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13 pages, 7144 KiB

Open AccessArticle

A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification

by Pengfei Ma, Yuesen Liu, Lin Li, Zhigang Zhu and Bin Li

Electronics 2023, 12(4), 920; https://doi.org/10.3390/electronics12040920 - 12 Feb 2023

Cited by 7 | Viewed by 2312

Abstract

Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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14 pages, 1058 KiB

Open AccessArticle

An Improved SVM with Earth Mover’s Distance Regularization and Its Application in Pattern Recognition

by Rui Feng, Haitao Dong, Xuri Li, Zhaochuang Gu, Runyang Tian and Houde Li

Electronics 2023, 12(3), 645; https://doi.org/10.3390/electronics12030645 - 28 Jan 2023

Cited by 1 | Viewed by 1218

Abstract

A support vector machine (SVM) aims to achieve an optimal hyperplane with a maximum interclass margin and has been widely utilized in pattern recognition. Traditionally, a SVM mainly considers the separability of boundary points (i.e., support vectors), while the underlying data structure information is commonly ignored. In this paper, an improved support vector machine with earth mover’s distance (EMD-SVM) is proposed. It can be regarded as an improved generalization of the standard SVM, and can automatically learn the distribution between the classes. To validate its performance, we discuss the necessity of the structural information of EMD-SVM in the linear and nonlinear cases, respectively. Experimental validation was designed and conducted in different application fields, which have shown its superior and robust performance. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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13 pages, 2567 KiB

Open AccessArticle

Automatic Modulation Classification Based on CNN and Multiple Kernel Maximum Mean Discrepancy

by Na Wang, Yunxia Liu, Liang Ma, Yang Yang and Hongjun Wang

Electronics 2023, 12(1), 66; https://doi.org/10.3390/electronics12010066 - 24 Dec 2022

Cited by 5 | Viewed by 2226

Abstract

Automatic modulation classification plays a significant role in numerous military and civilian applications. Deep learning methods have attracted increasing attention and achieved remarkable success in recent years. However, few methods can generalize well across changes in varying channel conditions and signal parameters. In this paper, based on an analysis of the challenging domain shift problem, we proposed a method that can simultaneously achieve good classification accuracy on well-annotated source data and unlabeled signals with varying symbol rates and sampling frequencies. Firstly, a convolutional neural network is utilized for feature extraction. Then, a multiple kernel maximum mean discrepancy layer is utilized to bridge the labeled source domain and unlabeled target domain. In addition, a real-world signal dataset consisting of eight digital modulation schemes is constructed to verify the effectiveness of the proposed method. Experimental results demonstrate that it outperforms state-of-the-art methods, achieving higher accuracy on both source and target datasets. Full article

(This article belongs to the Special Issue Machine Learning for Radar and Communication Signal Processing)

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