Resource management of opportunistic digital array radar antenna aperture for pattern synthesis. Issue 5 (1st May 2017)

• Record Type: Journal Article
• Authors: Han, Qinghua; Pan, Minghai; Gong, Shufeng; Long, Weijun
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 5(2017)
• Page Start: 829
• Page End: 837
• Publication Date: 2017-05-01
• Subjects: radar antennas -- antenna arrays -- antenna radiation patterns -- echo -- fuzzy set theory -- genetic algorithms; opportunistic digital array radar antenna aperture -- pattern synthesis -- resource management -- complex time‐varying environments -- echo signal -- fuzzy random chance‐constrained programming model -- nondominated sorting genetic algorithm -- NSGA_II -- hybrid intelligent optimisation algorithm; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0440
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Due to the complex time‐varying environments and the unknown target information, the uncertainty of target information is introduced into the echo signal. Owing to the uncertainty of target information, the number and operating modes of antenna array elements allocated for pattern synthesis are uncertain. Hence to reduce the influence of uncertainty of target information and save the antenna array elements, in the light of the uncertainty of the number and the distribution of the array elements in working state, a novel fuzzy random chance‐constrained programming model of opportunistic digital array radar antenna aperture resource management is proposed. Moreover, this model can determine the information of radar target from the echo signal by reasonable resource management and allocation under the condition that the specified confidence levels are satisfied. Meanwhile, for solving the multi‐object model, the fuzzy random simulation is integrated into fast and elitist non‐dominated sorting genetic algorithm (NSGA_II) to compose a hybrid intelligent optimisation algorithm. Finally, two simulations are carried out to verify the validity of this model.