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 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.