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Applied Sciences
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Applications of Machine Learning and Artificial Intelligence to Radar Signal...
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Applications of Machine Learning and Artificial Intelligence to Radar Signal Analysis and Interpretation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 4534

Special Issue Editor

Prof. Dr. Amit Kumar Mishra

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
Interests: sensor design; signal processing; machine learning; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding radar signals is a challenging task as humans do not have the sensory capability to directly perceive RF signals. Nevertheless, understanding radar signals is crucial for any application of radars, from crop classification to target recognition or electronic defence. In the last few years, tremendous progress has been achieved in the fields of machine learning and artificial intelligence. The application of these algorithms have proven to be very successful in multiple radar-based applications. This Special Issue aims to publish research efforts where ML and AI algorithms have been developed to understand radar signals for various applications. 

Prof. Dr. Amit Kumar Mishra
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • radar signal processing
  • phenomenology

Published Papers (3 papers)

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Research

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23 pages, 6838 KiB  
Article
Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
by Pingping Huang, Ran Wei, Yun Su and Weixian Tan
Appl. Sci. 2023, 13(17), 9793; https://doi.org/10.3390/app13179793 - 30 Aug 2023
Cited by 2 | Viewed by 1166
Abstract
Concealed object detection in millimeter wave (MMW) images has gained significant attention in the realm of public safety, primarily due to its distinctive advantages of non-hazardous and non-contact operation. However, this undertaking confronts substantial challenges in practical applications, owing to the inherent limitations [...] Read more.
Concealed object detection in millimeter wave (MMW) images has gained significant attention in the realm of public safety, primarily due to its distinctive advantages of non-hazardous and non-contact operation. However, this undertaking confronts substantial challenges in practical applications, owing to the inherent limitations of low imaging resolution, small concealed object size, intricate environmental noise, and the need for real-time performance. In this study, we propose Swin-YOLO, an innovative single-stage detection model built upon transformer layers. Our approach encompasses several key contributions. Firstly, the integration of Local Perception Swin Transform Layers (LPST Layers) enhanced the network’s capability to acquire contextual information and local awareness. Secondly, we introduced a novel feature fusion layer and a specialized prediction head for detecting small targets, effectively leveraging the network’s shallow feature information. Lastly, a coordinate attention (CA) module was seamlessly incorporated between the neck network and the detection head, augmenting the network’s sensitivity towards critical regions of small objects. To validate the efficacy and feasibility of our proposed method, we created a new MMW dataset containing a large number of small concealed objects and conducted comprehensive experiments to evaluate the effectiveness of overall and partial improvements, as well as computational efficiency. The results demonstrated a remarkable 4.7% improvement in the mean Average Precision (mAP) for Swin-YOLO compared with the YOLOv5 baseline. Moreover, when compared with other enhanced transformer-based models, Swin-YOLO exhibited a superior accuracy and the fastest inference speed. The proposed model showcases enhanced performance and holds promise for advancing the capabilities of real-world applications in public safety domains. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence to Radar Signal Analysis and Interpretation)
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20 pages, 7983 KiB  
Article
Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras
by Jiale Dai, Jiahui Yan and Yaolong Qi
Appl. Sci. 2023, 13(10), 6270; https://doi.org/10.3390/app13106270 - 20 May 2023
Viewed by 1318
Abstract
Millimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing [...] Read more.
Millimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing of the signals in the target environment is blind. We propose a method of using binocular vision to lock the distance dimension of the radar life signal and to determine the target distance by using the principle of the binocular camera parallax method, as this reduces the influence of the noise in the environment when determining the distance dimension of the target to be measured. First, the Yolo (you only look once: unified, real-time object detection) v5s neural network is used to call the binocular camera to detect the human body, where the resolution of the single lens is 1280 × 1200, and the DeepSORT (deep simple online real-time tracking) algorithm is used to extract the features of the target and track and register them. Additionally, the binocular vision parallax ranging method is used to detect the depth information of the target, search for the depth information in the range-dimensional FFT (frequency Fourier transform) spectrum of the radar echo signal, and take the spectral peak with the largest energy within the search range to determine it as the target. Then, the target is measured, the range gate of the target is determined, and the life signal is then separated through operations such as phase information extraction, unwrapping, and filtering. The test results showed that this method can be used to directionally separate and register corresponding life signals in a multiliving environment. By conducting an analysis using the Pearson correlation coefficient, we found that the correlation between the breathing frequency collected using this method and a breathing sensor reached 84.9%, and the correlation between the heartbeat frequency and smart bracelet results reached 93.6%. The target range gate was locked to separate and match the life signal. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence to Radar Signal Analysis and Interpretation)
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Review

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34 pages, 3044 KiB  
Review
A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar
by Konstantinos Papadopoulos and Mohieddine Jelali
Appl. Sci. 2023, 13(23), 12728; https://doi.org/10.3390/app132312728 - 27 Nov 2023
Viewed by 1205
Abstract
The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and [...] Read more.
The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence to Radar Signal Analysis and Interpretation)
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