Variational sparse Bayesian learning for optimal design of interleaved linear arrays with region constraint. Issue 8 (1st October 2016)

• Record Type: Journal Article
• Authors: Shen, Haiou; Wang, Buhong; Li, Longjun
• Is Part Of: IET radar, sonar & navigation. Volume 10:Issue 8(2016)
• Page Start: 1458
• Page End: 1467
• Publication Date: 2016-10-01
• Subjects: aperture antennas -- linear antenna arrays -- Bayes methods -- learning (artificial intelligence) -- electrical engineering computing -- vectors -- variational techniques -- optimisation -- design engineering; variational sparse Bayesian learning method -- optimal interleaved linear array design -- region constraint -- subarray elements -- array assembling -- minimum element spacing -- subarray interleaving -- sparse constrained optimisation problem -- relevance vector machine -- sparse weight vector estimation -- antenna elements -- representative numerical simulations -- shared aperture antennas; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2015.0567
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : An innovative approach based on the variational sparse Bayesian learning method is presented for the optimal design of interleaved linear arrays with a fewer number of subarray elements. In this study, with a consideration of practical array assembling, the acceptable minimum element spacing can be freely adjusted and used to construct the constrained regions for subarray interleaving. The design of interleaved sparse arrays is then recast as a sparse constrained optimisation problem under region constraint, and a fast relevance vector machine is utilised to estimate the sparse weight vectors, with which the number of the selected antenna elements as well as their positions and excitations can be obtained. A set of representative numerical simulations validate the high accuracy and computational efficiency of the proposed method.