Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective
Department of Electrical/Electronic Engineering, Nigeria Defence Academy, Kaduna, Nigeria.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 21(01), 119–129.
Article DOI: 10.30574/gjeta.2024.21.1.0189
Publication history:
Received on 03 September 2024; revised on 16 October 2024; accepted on 19 October 2024
Abstract:
This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.
Keywords:
Cognitive radar; Deep learning; YOLO; Mask R-CNN; Recurrent Neural Networks; Target detection; Target tracking
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