Pencil-based algorithms and grid-based compressed sensing (CS) algorithms have been developed in previous studies. The second category synthesises the desired radiation pattern while minimising the number of array elements. Nevertheless, these algorithms are usually time consuming due to numerous iterations. One is to reduce the sidelobe level (SLL) of the beam pattern by optimising the element positions and excitations, such as ant colony optimisation, genetic algorithm, particle swarm optimisation, differential evolution (DE), sequential convex optimisations, convex programing, inflating-deflating exploration algorithm etc. The algorithms proposed to design the SLA can be divided into two categories according to different objective functions. Therefore, it is critical to synthesise the SLA with a satisfactory beam pattern. However, the synthesis of the SLA is not a simple matter because inaccurate array element positions and excitations will lead to performance degradation of the beam pattern. Compared with the uniform linear array, the sparse linear array (SLA) has advantages in terms of overall design complexity, hardware cost, mutual coupling effect, power consumption etc. Pattern synthesis of sparse antenna array has been widely applied in many applications (e.g. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
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