基于稀疏最小二乘支持向量回归的非线性自适应波束形成

Non-linear Adaptive Beamforming Method Using Sparse Least Squares Support Vector Regression

该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。

A nonlinear adaptive beamforming approach based on Least-Square Support Vector Regression （LS-SVR） is proposed to enhance the beamformer＇s robustness against array model mismatch, constrained samples numerous interferences, etc. The approach has two highlights, one is a recursive regression procedure to compute the regression parameters on real-time, the other is a sparse mode based on novelty criterion, which can significantly reduce the size of the input samples. Applying the sparse model to LS-SVR beamforming leads to reduced computation complexity and better generalization capacity. The theory analysis and experimental results show that the proposed beamforming approach could improve array performance significantly over several classical linear beamforming methods.