Target Detection, Classification, and Tracking Based on Statistical Signal/Image Processing and Machine Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (6 January 2025) | Viewed by 101
Special Issue Editors
Interests: variational bayes; compressive sensing; statistical signal processing; machine learning; autonomous drones
Interests: digital communications; error correction coding; signal processing; detection and estimation theory; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) presents new possibilities for dealing with topics that have been treated with conventional signal/image processing techniques, such as target detection, classification, and tracking. ML opens the possibility, for example, of employing nonlinear methods where, in the past, linear optimal methods have been developed, raising the question of what nonlinear methods offer that previous methods lacked. This Special Issue welcomes both theoretical and applied explorations of questions related to ML and signal/image processing. New developments in ML techniques are of particular interest.
This Special Issue also targets original work related to how statistical techniques combined with machine learning algorithms are revolutionizing the detection and analysis of various types of targets in different domains, such as defense, surveillance, healthcare, and environmental monitoring. This Special Issue will be of interest to researchers in neural networks, machine learning, signal and image processing, and technical topics such as target detection, classification, tracking, radar systems, and closely related topics.
Dr. Mohammad Shekaramiz
Prof. Dr. Todd K. Moon
Dr. Majid Memari
Guest Editors
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Keywords
- detection theory
- estimation theory
- machine learning
- image processing
- pattern recognition
- target tracking
- target detection
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