基于协方差矩阵估计的稳健Capon波束形成算法

常规Capon波束形成器性能对模型误差或失配非常敏感,尤其是当期望信号包含在训练数据中,导向矢量失配将引起性能急剧下降。为解决这一问题,提出了一种采用干扰噪声协方差矩阵和导向矢量联合估计的稳健波束形成算法。该方法通过对Capon空间谱在非目标信号的方位区域内的积分,实现对干扰噪声协方差矩阵的估计,解决数据协方差矩阵包含有目标信号时引起信号自相消问题;其次为了克服导向矢量失配的影响,通过最大化输出功率,并增加二次型约束防止估计的导向矢量接近于干扰导向矢量,实现对导向矢量的估计。仿真实验表明:该算法能获得近似最优的输出信干噪比,与现有算法相比稳健性更强。

Robust Capon Beamforming Algorithm Based on Covariance Matrix Estimation

Adaptive beamformers are sensitive to model mismatch,especially when the desired signal is present in training snapshots or when the training is done using data samples. The performance of Capon beamformer degrades sharply in the presence of array steering vector mismatch. To solve this problem, a robust beamforming algorithm based on interference covariance matrix reconstruction and steering vector estimations is proposed. Firstly, this method is based on the reconstruction of the covariance matrix which aims to reduce the power of the signal of interest(SOI) in the covariance matrix. The estimator is based on the Capon spectral estimator integrated over a region separated from the desired signal direction. Subsequently,the mismatch in the steering vector of the desired signal is estimated by maximizing the beamformer output power under a quadratic constraint that prevents the corrected steering vector from getting close to the interference steering vectors. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always close to the optimal value and the superiority of the proposed method over other previously developed robust adaptive beamforming techniques.