基于自调整映射法则的矩形稀布阵列优化方法

An Optimization Method of Rectangular Sparse Array Based on Self-adjusting Mapping Rule

针对多约束条件下的矩形稀布阵列优化问题,提出了一种基于自调整映射法则的矩形稀布阵列优化方法。该方法首先提出一种新的最优化问题用于确定阵元坐标矩阵维数以平衡放置阵元数的自由度和阵列孔径可调整空间,并引入用于稀疏阵元的阵元状态矩阵;其次,构建自调整映射法则,对矩形阵列不同方向进行相应的映射,并对阵列短轴方向进行自调整,使得阵元位置满足最小阵元间距,避免出现不可行解;然后,通过粒子群优化算法优化阵元位置得到最优阵元坐标;最后,考虑适应度函数为单波束指向和多波束指向,对算法进行仿真验证。仿真结果发现该算法可以满足多约束条件并有效降低阵列的峰值旁瓣电平,从而提高矩形稀布阵列的性能。

To solve the optimization problem of rectangular sparse array under multiple constraints,an optimization method of rectangular sparse array based on self-adjusting mapping rule is proposed.Firstly,a new optimization problem is proposed to determine the dimensions of the array element coordinate matrix to balance the freedom degree of array element number and the adjustable space of array aperture,and the array element state matrix for sparse array elements is introduced.Secondly,a self-adjusting mapping rule is constructed to map different directions of the rectangular array,and the direction of the short axis of the array is self-adjusted to make the positions of the array elements so as to meet the minimum array element spacing and avoid infeasible solutions.Thirdly,the positions of the array elements are optimized by the particle swarm optimization algorithm to obtain the optimal array element coordinates.Finally,considering that the fitness function is single-beam pointing and multi-beam pointing,the algorithm is simulated and verified.Through the simulation results,it is found that the algorithm can meet multiple constraints and effectively reduce the peak sidelobe level of the array,thereby improving the rectangular sparse array performance.