Two-dimensional Nested Optimization Method for Sparse Linear Array

The study on sparse array and sparse array design methods show that this type of methods can effectively suppress grating lobes phenomenon, save cost, and reduce system complexity. Aiming at the phenomenon of grating lobes observed in uniform linear arrays, an array model incorporating two-dimensional spatial information of array elements is proposed in this paper. By introducing two-dimensional perturbations, the spatial distribution of array elements is extended from the array axis direction to the array axis normal direction, mitigating the constraints imposed by the minimum element spacing and expanding the search space for element position parameters. An iterative optimization framework for element spatial position parameters is constructed using a metaheuristic algorithm, alternately optimizing the two-dimensional spatial information of array elements to enhance the non-periodicity of the array and suppress the generation of grating lobes. Experimental results demonstrate that, under the conditions of a given array aperture, element count, and the occurrence of grating lobes in a corresponding uniform linear array, the proposed approach reduces the peak sidelobe level by 3.04 dB compared to the standard genetic algorithm and the basic particle swarm optimization optimized only in the array axis direction.