基于Bayesian方法的鲁棒约束最小二乘恒模算法

Robust Constrained LSCMA Based on a Bayesian Approach

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.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,利用权重向量的模值约束条件推导出其迭代公式,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.所提鲁棒波束形成算法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.仿真实验结果表明,与线性约束LSCMA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.

In practical communication situations,the performance of linearly constrained least squares constant modulus algorithm（LSCMA） is known to degrade severely in the presence of even slight signal steering vector mismatches.In view of this problem,a novel robust constrained LSCMA based on Bayesian approach is proposed.The proposed algorithm estimates direction of arrival（DOA） of the actual signal from the observed data and priori knowledge about the source DOA.The iterative formula of the weight vector is derived by the norm constraint condition.The proposed algorithm provides excellent robustness against deviation of DOA,thus the uncertainty in the DOA of the actual signal is reduced,and the algorithm makes the output array SINR close to the optimal value.The proposed robust adaptive beamforming algorithm can reduce the computational complexity for using the recursive method to obtain inverse matrix and be implemented in real-time situations.Computer simulation results demonstrate that the proposed algorithm can outperform linearly constrained LSCMA in the complex communication environment.