Optimal MIMO Sparse Array Design Based on Simulated Annealing Particle Swarm Optimization

Compared to conventional uniformly spaced arrays, sparse arrays are effective and economical for many application scenarios. For example, sparse arrays can lower the peak sidelobe level (PSLL) or narrow the main lobe half-power 3dB beamwidth (3dB BW) of the array pattern with fewer elements. This can help to save on the number of antennas, and the supporting hardware costs, as well as improve the angular resolution which is useful for automotive radar systems and target identification applications. Furthermore, when fewer elements are employed, as in sparse arrays, the spacing between the elements can become larger, which can lead to a reduction in the mutual coupling. This paper will focus on implementing a hybrid approach to numerically determine an optimal 4x4 multiple-input multiple-output (MIMO) array considering simulated annealing and particle swarm optimization (SA-PSO). Results will show that the technique can achieve lower PSLL or narrower 3dB BW with 8 fewer elements when compared to a corresponding 16 element uniform linear array (ULA). To the best knowledge of the authors, this is the first time that such comparisons and design techniques have been applied to sparse MIMO arrays whilst considering a hybrid SA-PSO synthesis algorithm.