自适应波束形成算法性能优化研究

Research on Performance Optimization of Adaptive Beam-Forming Algorithm

自适应波束形成算法是信号源定位的关键技术,影响自适应波束形成算法性能的主要因素是算法的收敛速度和稳定性,良好的自适应算法收敛速度快、计算复杂度低和有稳定鲁棒性。针对传统自适应波束形成算法收敛速度慢和抗干扰性能差的问题,通过理论推导和仿真对比分析最小均方算法(LMS)和递推最小二乘算法(RLS)的性能,并提出一种改进的RLS算法。通过施加二次型约束,对期望信号波达方向附近范围内的方向向量的误差值进行约束,来提高算法的鲁棒性,并在约束条件下对权重向量进行优化求解,经Matlab仿真分析,结果表明改进算法有更快的收敛速度和更好抗干扰性能。

Adaptive beamforming is a key technology of signal source localization, and the main factors influen- cing the performance of the adaptive beamforming algorithm are the convergence speed and stability of the algorithm, Good adaptive algorithm convergence fast, and have low arithmetic complexity and good stability robustness. The pa- per focuses on least mean squares （LMS） algorithm and recursive least-squares （RLS） algorithm, analyzes their anti -interference ability and convergence,and put forward a improved RLS algorithm. Applying quadratic constraints to improve robustness, the weight vector was optimized to involve minimization of a quadratic function subject to the norm of error between the actual and assumed signal steering vectors, and the parameter in the optimal solution can be solved accurately. Analyzing the performance of the improved algorithm based on Matlab, the results show that the proposed algorithm has faster convergence speed and better anti-jamming performance.