基于Bayesian方法的鲁棒约束最小二乘恒模算法

在实际通信环境中，信号方向向量偏差会导致线性约束最小二乘恒模算法(LSCMA)的性能急剧下降，针对这一问题，提出了基于Bayesian方法的鲁棒约束LSCMA算法并对其性能进行了分析。该算法根据接收到的采样信号和信号的先验信息对实际信号方向向量进行估计，进而推导出权重向量的迭代公式，降低了信号波达方向的不确定性，对信号方向向量偏差具有较强的鲁棒性，从而可以保证阵列输出的信干噪比接近最优值。本文采用递推方法来计算逆矩阵，大大地降低了计算复杂度，能够满足实时处理的要求。仿真实验结果与理论分析表明，与线性约束LSCMA算法相比，所提鲁棒自适应波束形成算法具有更好的性能，且能适应实际复杂的通信环境。

Robust Constrained LSCMA based on a Bayesian Approach

When adaptive arrays are applied to practical situations, the performance of linear constrained least squares constant modulus algorithm (LSCMA) is known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. To account for mismatches, a novel robust constrained LSCMA using a Bayesian approach is proposed in this paper. The proposed algorithm attempts to respond to the current environment by learning or estimating direction of arrival (DOA) of the actual signal from the observed data and a priori knowledge about the source DOA. The proposed robust adaptive beamforming algorithm can be efficiently computed at a comparable cost with other algorithms for using the recursive method to obtain inverse matrix. The proposed algorithm provides excellent robustness against uncertainty in the DOA of the actual signal and makes the mean output array SINR consistently close to the optimal one. Computer simulation results demonstrate the performance of the proposed algorithm can outperform linear constrained LSCMA algorithm in the complex communication environment.