Hybrid Sparse Array Design for Under-determined Models

Sparse arrays are typically configured considering either the environmental dependent or independent design objectives. In this paper, we investigate hybrid sparse array design satisfying dual design objectives. We consider enhancing the source identifiability and maximizing the Signal-to-Interferen...

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Main Authors: Hamza, Syed A., Amin, Moeness G.
Format: Conference Proceeding
Language:English
Subjects:
Array signal processing
Correlation
fully augmentable sparse arrays
Interference
l 1 -norm
MaxSINR
Optimization
QCQP
Sensor arrays
Signal to noise ratio
SINR
Sparse matrices
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Amin, Moeness G.
description Sparse arrays are typically configured considering either the environmental dependent or independent design objectives. In this paper, we investigate hybrid sparse array design satisfying dual design objectives. We consider enhancing the source identifiability and maximizing the Signal-to-Interference-plus-noise-ratio (SINR) as our design criteria. We pose the problem as designing fully augmentable sparse arrays for receive beamforming achieving maximum SINR (MaxSINR) for desired point sources operating in an interference active environment. The problem is formulated as a re-weighted l 1 -norm squared quadratically constraint quadratic program (QCQP). Simulation results are presented to show the effectiveness of the proposed algorithm for designing fully augmentable arrays in case of under-determined scenarios.
doi_str_mv 10.1109/ICASSP.2019.8682266
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subjects Array signal processing
Correlation
fully augmentable sparse arrays
Interference
l 1 -norm
MaxSINR
Optimization
QCQP
Sensor arrays
Signal to noise ratio
SINR
Sparse matrices
title Hybrid Sparse Array Design for Under-determined Models
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