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We consider sparse array beamfomer design achieving maximum signal-tointerference plus noise ratio (MaxSINR). Both array configuration and weightsare attuned to the changing sensing environment. This is accomplished bysimultaneously switching among antenna positions and adjusting thecorresponding weights. The sparse array optimization design requires estimatingthe data autocorrelations at all spatial lags across the array aperture.Towards this end, we adopt low rank matrix completion under the semidefiniteToeplitz constraint for interpolating those autocorrelation valuescorresponding to the missing lags. We compare the performance of matrixcompletion approach with that of the fully augmentable sparse array designacting on the same objective function. The optimization tool employed is theregularized $l_1$-norm successive convex approximation (SCA). Design exampleswith simulated data are presented using different operating scenarios, alongwith performance comparisons among various configurations.

Sparse Array Design for Maximizing the Signal-to-Interference-plus-Noise-Ratio by Matrix Completion