Sparse Bayesian learning for spinning antenna DOA super‐resolution. Issue 6 (1st March 2018)

• Record Type: Journal Article
• Authors: Alibrahim, Fuad; Inggs, Michael
• Is Part Of: Electronics letters. Volume 54:Issue 6(2018)
• Page Start: 389
• Page End: 391
• Publication Date: 2018-03-01
• Subjects: iterative methods -- learning (artificial intelligence) -- Monte Carlo methods -- adaptive antenna arrays -- direction‐of‐arrival estimation -- image resolution -- array signal processing -- Bayes methods; spinning bandwidth antenna -- wide bandwidth antenna -- cost‐effective technique -- direction of arrival -- emitters -- low edge -- monitored band -- poor angular resolution -- low frequencies -- parameter‐free angular super‐resolution algorithm -- spinning antenna based system -- sparse Bayesian learning technique -- iterative adaptive approach algorithm; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2017.4010
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : The spinning, wide bandwidth antenna remains the most cost‐effective technique for finding the direction of arrival of emitters' signals. The beam is broadest at the low edge of the monitored band, resulting in poor angular resolution at low frequencies. A parameter‐free angular super‐resolution algorithm is proposed to find the direction of arrival of signals impinging on a spinning antenna based system that does not require tuning by the user. The proposed algorithm was constructed by using the sparse Bayesian learning technique. Using Monte Carlo simulation, the performance of the proposed algorithm is evaluated and shows that it outperforms the iterative adaptive approach algorithm.