Sensor selection for sparse source detection in planar arrays

Sensor selection for sparse source detection in planar arrays

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The sensor selection is a technique to reduce the cost, energy consumption, and complexity of a system by discarding redundant or less useful sensors. In this technique, one attempts to select a subset of sensors within a larger set so as to optimise a performance criterion. In this work, the authors consider the detection of sources in the 3D space when the source space could be sparsely represented. The application of compressive sensing (CS) methods in this setting has been extensively studied. Their aim in this work is to carry out the task of sensor selection in a planar array without sacrificing the detection performance. Their approach is to adopt the equivalent CS model and reduce the mutual coherence of the associated sensing matrix. This minimisation task is non-convex, for which they propose a convex relaxation. Numerical simulations demonstrate that the gap between their relaxed solution and its optimal counterpart is negligible. The proposed sensor selection method is also shown to outperform the uniform approach in terms of source detection.

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