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

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.