Recursive Sparsity-based MVDR Algorithm for Interference Cancellation in Sensor Arrays

ABSTRACT

In this paper, an improved sparsity-based interference cancellation algorithm for beamforming in sensor arrays is proposed, which improves the conventional Minimum Variance Distortionless Response (MVDR) algorithms. The key point in this algorithm is to divide the stress on the minimum variance term in the conventional algorithms with a term that uses no additional information than those used in conventional algorithms. The goal in this algorithm is to find the sparsest array response in the angles other than the Direction Of Arrival (DOA) of the Signal Of Interest (SOI). Also, a global convergence proof for this algorithm is presented. The results show that by this algorithm, some reduction in sidelobe levels, deeper nulls, less sensitivity to the DOA of the SOI and no sensitivity to data length are achieved.

KEYWORDS:

• Beamforming
• interference cancellation
• sparsity
• sparse recovery
• minimum variance distortionless response
• direction of arrival

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

M. Ghadian

Mohammad Ghadian received the BS and the MSc degree in electrical engineering from Iran University of Science and Technology, Tehran. His current research interests are in the areas of array signal processing, radar signal processing, and sparse reconstruction.

Email: [email protected]

M. Jabbarian-Jahromi

Mohammad Jabbarian-Jahromi received the BS degree in electrical engineering from Shiraz University, Shiraz, Iran, in 2005 and the M.Sc. degree from the Electrical and Electronic Engineering University Complex, Tehran, Iran, in 2008. He is currently working toward the PhD degree in the School of Electrical Engineering, Iran University of Science and Technology. From 2009, he was a research staff in the Signal & System Modelling laboratory, IUST. His current research interests are in the areas of array signal processing, radar signal processing, and sparse reconstruction.

Email: [email protected]

M. H. Kahaei

Mohammad Hossein Kahaei received the BSc degree from Isfahan University of Technology, Isfahan, Iran, in 1986, the MSc degree from the University of the Ryukyus, Okinawa, Japan, in 1994, and the PhD degree in signal processing from the School of Electrical and Electronic Systems Engineering, Queensland University of Technology, Brisbane, Australia, in 1998. Since 1999, he has been with the School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran, where he is an Associate Professor and Head of the Signal & System Modeling laboratory. His research interests include array signal processing with primary emphasis on compressed sensing, blind source separation, localization, tracking, and DOA estimation, and wireless sensor networks.

Email: [email protected]