稳健的子带子阵级导向最小方差波束形成算法

导向最小方差(STMV)波束形成是一种利用导向协方差矩阵获得自适应权值的方法,具有快速收敛特性.常规的稳健导向最小方差(RSTMV)波束形成算法在处理宽频带信号时,性能下降明显.为了改善算法的性能,结合频域子带划分和空域子阵划分技术,提出一种多子带不确定集独立约束的稳健子阵级STMV波束形成算法。通过频域子带划分可对不同子带的导向向量误差范数边界进行约束,计算出各子带对应的对角加载量,得到稳健的子带级最小方差波束形成算法权向量;同时采用子阵技术进行降维处理,可进一步增加划分子带的数目,从而提高算法的性能并有效降低计算复杂度,最终得到一种稳健的子带子阵级STMV波束形成算法。理论分析和仿真结果表明,在阵列导向向量存在误差的情况下,该算法在干扰方向形成的零陷最深,且零陷波束宽度最窄,输出信噪比接近理论值,因此性能最佳.实际海试数据处理表明,在强干扰目标存在时,弱目标输出信干噪比较RSTMV算法可提高4 dB,较常规波束形成可提高10 dB,在角度分辨力和算法复杂度方面得到有效改善,同时可以保证目标功率无失真输出。 

A robust subband subarray steered minimum variance beamforming algorithm

The STeered Minimum Variance(STMV) beamforming was a fast-convergent adaptive algorithm,the weight of which are obtained using steered covariance matrix.The performance of traditional Robust STeered Minimum Variance(RSTMV) beamforming will degrade obviously for wideband signals.In order to improve the performance of RSTMV,a robust subband subarray STMV beamforming algorithm is proposed in which the frequency domain subband and spatial domain subarray techniques are combined.The weight vector can be obtained using appropriate diagonal loading amount through constraining every subband's boundary of Array Steering Vector(ASV) errors' norm.The number of subbands can be futher increased using the subarray techniques,so the performance can be obviously improved and the complexity can be effectively reduced.The theoretical analysis and simulations show the new algorithm has deepest nulling and narrowest beam width,the output Signal to Interference and Noise Ratio(SINR) was close to the theoretical value,so the performance of this algorithm was best when ASV errors exist.The results of trial data show that the output SINR of the proposed algorithm can be increased by 10 dB relative to the Conventional Beamforming(CBF) and 4 dB relative to the RSTMV beamforming when there is strong interference.At the same time,the computational complexity and resolution can be effectively improved,the power of targets can be obtained distortionlessly.