自适应波束形成算法性能优化研究
发布时间：2018-07-27 18:52
【摘要】：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。
[Abstract]:Adaptive beamforming algorithm is the key technology of signal source location. The main factor affecting the performance of adaptive beamforming algorithm is the convergence speed and stability of the algorithm. Low computational complexity and stable robustness. Aiming at the problems of slow convergence speed and poor anti-jamming performance of traditional adaptive beamforming algorithm, the performance of minimum mean square algorithm (LMS) and recursive least square algorithm (RLS) are analyzed by theoretical derivation and simulation, and an improved RLS algorithm is proposed. In order to improve the robustness of the algorithm, the quadratic constraint is applied to constrain the error of the direction vector in the range of the direction of arrival of the desired signal, and the weight vector is optimized under the constraint conditions, and analyzed by Matlab simulation. The results show that the improved algorithm has faster convergence speed and better anti-jamming performance.
【作者单位】： 上海工程技术大学汽车工程学院;
【基金】：国家自然科学基金项目(51675324,51175320) 上海市自然科学基金项目(14ZR1418600)
【分类号】：TN911.7
本文编号：2148823
[Abstract]:Adaptive beamforming algorithm is the key technology of signal source location. The main factor affecting the performance of adaptive beamforming algorithm is the convergence speed and stability of the algorithm. Low computational complexity and stable robustness. Aiming at the problems of slow convergence speed and poor anti-jamming performance of traditional adaptive beamforming algorithm, the performance of minimum mean square algorithm (LMS) and recursive least square algorithm (RLS) are analyzed by theoretical derivation and simulation, and an improved RLS algorithm is proposed. In order to improve the robustness of the algorithm, the quadratic constraint is applied to constrain the error of the direction vector in the range of the direction of arrival of the desired signal, and the weight vector is optimized under the constraint conditions, and analyzed by Matlab simulation. The results show that the improved algorithm has faster convergence speed and better anti-jamming performance.
【作者单位】： 上海工程技术大学汽车工程学院;
【基金】：国家自然科学基金项目(51675324,51175320) 上海市自然科学基金项目(14ZR1418600)
【分类号】：TN911.7
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