Compressed sensing approach for pattern synthesis of maximally sparse non-uniform linear array

Compressed sensing (CS) has been successfully applied to the synthesis of maximally sparse non-uniform linear array with the synthesised pattern matching the reference pattern very well by using as few elements as possible. According to the CS theory, a sparse or compressible high-dimensional signal can be first projected onto a low-dimensional space through a measurement matrix, and then recovered accurately by using a variety of practical algorithms based on the low-dimensional information. The proposed approach can synthesise the sparse linear arrays fitting the desired patterns with a minimum number of elements. Numerical simulations validate the effectiveness and advantages of the proposed synthesis method. Moreover, compared with the existing sparse-array synthesis methods, the author´s method is more robust and accurate, while maintaining the advantage of easy implementation.