高阶累积量自适应波束形成的改进算法

针对线性约束最小方差(LCMV)算法在自适应波束形成时,存在的对噪声敏感、信噪比(SNR)较高时波束形成受小特征值扰动影响较大的情况。在基于高阶累积量的LCMV算法的基础上提出改进方法。该方法首先计算阵列接收数据的高阶累积量,然后对高阶累积量构造数据增广矩阵,进行奇异值分解求出伪逆,再用伪逆修正LCMV算法的权值,形成波束。仿真结果表明,相比于传统LCMV算法与基于高阶累积量的LCMV算法。算法能够有效地克服信噪比升高时小特征值扰动对波束形成的不良影响,且在较低快拍数下仍能有效形成波束。

Improved Adaptive Beamforming Algorithm Based on Higher Order Cumulant

For the Linear Constrained Minimum Variance (LCMV) algorithm in adaptive beamforming,there are sensitive to noise and the Signal-to-Noise Ratio(SNR)when beam forming by the small eigenvalue perturbation influence situation. In this paper,an improving method is proposed based on LCMV algorithm of high order cumulant . The method first calculates the higher-order cumulants of the received data of array,then the high order cumulant structure data augmented matrix singular value decomposition to calculate the pseudo inverse ,and pseudo inverse weight correction LCMV ,adaptive beamforming. The simulation results show that compared to the traditional LCMV algorithm and LCMV algorithm based on higher order cumulants. This algorithm can effectively overcome the adverse effects of disturbance on the signal to noise ratio increased when the beam forming small eigenvalues,and at a relatively low number of snapshots is still effective beamforming.