基于半正定秩松弛方法的稳健波束形成

发布时间：2018-11-06 14:46

【摘要】：现有的向量加权稳健波束形成方法只有在指向误差较小的情况下才能有效估计目标的信号功率;矩阵加权波束形成方法在指向误差较大时,虽然可以估计目标的信号功率,但是它的系统实现复杂度与向量加权稳健波束形成方法相比较大。针对以上问题,该文提出基于半正定秩松弛(SDR)方法的稳健波束形成,该方法优化模型中的目标函数与Capon算法的目标函数相同,优化变量为加权向量的协方差矩阵,并约束方向图的主瓣幅度波动范围、旁瓣电平,协方差矩阵的秩为1。应用SDR方法求得加权向量的协方差矩阵,将该矩阵中的每一行(列)转化为加权向量,然后选择使得方向图主瓣与0 dB之间失真最大值最小的一个加权向量。该方法的系统实现复杂度与传统向量加权方法一致,对信号功率的估计性能与矩阵加权方法相当。仿真实验验证了该文方法可以得到理想的方向图形状,并且可以在大指向误差条件下有效估计信号功率。
[Abstract]:The existing vector-weighted robust beamforming methods can effectively estimate the signal power of the target only when the pointing error is small. Although the signal power of the target can be estimated when the pointing error of the matrix weighted beamforming method is large, its system implementation complexity is larger than that of the vector weighted robust beamforming method. In this paper, robust beamforming based on positive semidefinite rank relaxation (SDR) method is proposed. The objective function of the optimization model is the same as the objective function of the Capon algorithm, and the optimization variable is the covariance matrix of the weighted vector. The magnitude fluctuation range of the principal lobe, the sidelobe level, and the rank of covariance matrix are 1. The covariance matrix of the weighted vector is obtained by using the SDR method, and each row (column) in the matrix is transformed into a weighted vector, and then a weighted vector is selected to minimize the maximum distortion between the principal lobe of the pattern and 0 dB. The system implementation complexity of this method is consistent with the traditional vector weighting method, and the estimation performance of the signal power is equivalent to that of the matrix weighting method. The simulation results show that the proposed method can obtain the ideal pattern shape and can effectively estimate the signal power under the condition of large pointing error.
【作者单位】：西安电子科技大学雷达信号处理国家重点实验室;
【基金】：国家自然科学基金(61271291,61201285) 新世纪优秀人才支持计划(NCET-09-0630) 全国优秀博士学位论文作者专项资金(FANEDD-201156) 中央高校基本科研业务费专项资金资助课题
【分类号】：TN911.7

【参考文献】