New Advances in Radar Target Intelligent Recognition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: 20 November 2024 | Viewed by 90
Special Issue Editors
Interests: radar target recognition; radar target detection; radar signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: synthetic aperture radar; deep learning; sparse signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: statistical signal processing; machine learning; radar image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Radar is a kind of very powerful microwave sensor system which can obtain the information of all-day and all-weather targets and plays an important role in various fields related to social safety. Currently, radars can not only obtain the position, velocity, and scattering intensity of the target, but also provide multi-dimensional measurements and high-resolution target images with the help of various advanced system designs and signal processing methods. Hence, target detection function and the decision categories of real-world targets from radar returns have received significant attention. Radar target recognition technology, based on machine learning and artificial intelligence, can automatically and quickly extract the features of the targets and identify their categories, which has become one of the most attractive but challenging tasks in radar applications.
This Special Issue focuses on advanced radar target recognition technologies and systems, the most recent radar target recognition theories based on artificial intelligence, and their innovative applications in a variety of fields. Therefore, it is our pleasure to invite you all to contribute to this Special Issue of Applied Sciences. The topics of interest for this Special Issue include, but are not limited to, the following:
- Radar space/aerial target recognition;
- Radar ground target recognition;
- Radar sea surface target recognition;
- Target micro-motion feature extraction;
- Polarimetric radar target recognition;
- Synthetic aperture radar (SAR) target detection and recognition;
- Radar target characteristics analysis;
- Multimodal target recognition methods;
- New theories and methods of target detection and recognition;
- Radar target recognition systems.
Dr. Jifang Pei
Prof. Dr. Wei Pu
Dr. Weibo Huo
Guest Editors
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
- radar
- target recognition
- target detection
- feature extraction
- target characteristics
- machine learning
- deep neural networks
- artificial intelligence
