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http://dx.doi.org/10.5515/KJKIEES.2018.29.1.68

Target Prioritization for Multi-Function Radar Using Artificial Neural Network Based on Steepest Descent Method  

Jeong, Nam-Hoon (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Lee, Seong-Hyeon (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Kang, Min-Seok (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Gu, Chang-Woo (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Kim, Cheol-Ho (Agency for Defense Development)
Kim, Kyung-Tae (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.29, no.1, 2018 , pp. 68-76 More about this Journal
Abstract
Target prioritization is necessary for a multifunction radar(MFR) to track an important target and manage the resources of the radar platform efficiently. In this paper, we consider an artificial neural network(ANN) model that calculates the priority of the target. Furthermore, we propose a neural network learning algorithm based on the steepest descent method, which is more suitable for target prioritization by combining the conventional gradient descent method. Several simulation results show that the proposed scheme is much more superior to the traditional neural network model from analyzing the training data accuracy and the output priority relevance of the test scenarios.
Keywords
Radar Resource Management; Target Prioritization; Neural Network;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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