基于近似梯度下降的稀疏阵列综合方法

Synthesis of sparse array based on approximate gradient descent

在建立对均匀激励稀疏线阵优化模型的基础上,通过充分利用局部解析性质,设计了一种基于近似梯度下降的迭代构型寻优方法。该方法可以高效地对不同尺度的稀疏初始构型进行连续微调,进而对高旁瓣进行逐个抑制。通过数值仿真验证了算法的优化效果,结果表明该算法可用远优于启发式算法的效率完成方向图旁瓣抑制。

Synthesis of unequally spaced linear arrays with uniform excitation is considered.Based on the establishment of the corresponding optimization model,an iterative synthesis algorithm based on approximate gradient descent is designed by making full use of the local analytic properties,which can efficiently fine-tune the initial geometry of different scales,suppressing the high side lobes in order.The effect of the algorithm is verified by numerical simulation,and the results show that the proposed algorithm can achieve side lobe suppression with much better efficiency than the traditional heuristic algorithms.