Pattern synthesis of the distributed array based on the hybrid algorithm of particle swarm optimization and convex optimization

Abstract

To solve the high peak side-lobe level of the distributed array, a hybrid optimization method of particle swarm optimization and convex optimization is proposed in this paper. With the peak side-lobe level as the objective function, the particle swarm optimization is considered as a global optimization algorithm to optimize the elements' positions while the convex optimization is considered as a local optimization algorithm to optimize the elements' weights. In this algorithm, the reducing of the variables' dimensions and the complete match of positions and weights for every particle improve the optimal performance effectively. The results show that for a distributed linear array, the algorithm proposed in this paper can obtain a lower peak side-lobe level under the constraint of main lobe width and limited number of array elements. The better performance of pattern synthesis demonstrates the effectiveness of the algorithm.