Machine Learning for Radar and Communication Signal Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".
Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 9755
Special Issue Editors
Interests: nonstationary signal analysis; radar emitter classification; source separation; signal processing; deep learning
Interests: signal processing; autonomous navigation; brain-inspired artificial intelligence
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
Manuscript Submission Information
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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