Sparse Bayesian learning for spinning antenna DOA super-resolution

Fuad Alibrahim; Michael Inggs

Sparse Bayesian learning for spinning antenna DOA super-resolution

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Author(s): Fuad Alibrahim¹ and Michael Inggs²
- Affiliations: 1: King Abdulaziz City for Science and Technology , Riyadh , Saudi Arabia ;
  2: Department of Electrical Engineering , University of Cape Town , Rondebosch , South Africa
Source: Volume 54, Issue 6, 22 March 2018, p. 389 – 391
DOI: 10.1049/el.2017.4010 , Print ISSN 0013-5194, Online ISSN 1350-911X

Received 02/11/2017, Published 15/02/2018

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.

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