基于量子粒子群算法的大型阵稀疏优化方法

Sparse Optimization Method for Large Arrays Based on Quantum Particle Swarm Optimization

本文提出了一种基于量子粒子群算法(quantum particle swarm optimization,QPSO)的大型阵稀疏优化方法。该方法在约束主瓣宽度的条件下,以阵列的阵元位置和节点的相位中心为优化参量,以方向图的峰值副瓣电平为优化目标,有效结合了QPSO算法,并将其应用于大型阵的稀疏优化。相对于传统稀疏优化方法,本文所提方法不受更新速度和轨迹的约束,并提高了全局搜索能力、加快了收敛速度。仿真结果验证了该方法的有效性。

In this paper, we propose a sparse optimization method for large arrays based on Quantum Particle Swarm Optimization(QPSO). Under the constraint of the main lobe width, this method takes the array element position and the node phase center as optimization parameters, and uses the peak side-lobe level of the pattern as the optimization target. It effectively combines the QPSO algorithm and will be applied for sparse optimization for large arrays. Compared with the traditional sparse optimization method, the method proposed in this paper is not restricted by the update speed and trajectory, and it improves the global search ability and accelerates the convergence speed.Simulation results verify the effectiveness of the method.