Compressive sensing-based approach to the design of linear robust sparse antenna arrays with physical size constraint

In sparse arrays, the randomness of antenna locations avoids the introduction of grating lobes, while allowing adjacent antenna spacings to be greater than half a wavelength. This means a larger array size can be implemented using a relatively small number of antennas. However, careful consideration has to be given to antenna locations to ensure that an acceptable performance level is achieved. Model perturbations can also cause steering vector errors, which in turn cause discrepancies in the array´s response, making robust arrays desirable. This study presents various compressive sensing-based methods that can solve this problem, while also imposing the antenna size as a constraint on the minimum adjacent antenna separations. Narrowband and multiband design examples are presented to verify the effectiveness of the proposed design methods, with comparisons being drawn with a previously proposed genetic algorithm-based approach.