基于干扰噪声矩阵重构的自适应波束形成算法

针对在自适应波束形成中，当采样次数较少或期望信号导向矢量存在误差以及训练数据中含有期望信号成分时导致波束输出信干噪比(SINR)下降的问题，提出了一种重构干扰噪声协方差矩阵并且估计期望信号导向矢量的稳健自适应波束形成方法。在期望信号波达方向的角度范围已知的条件下，首先利用多重信号分类（MUSIC）空间谱在不含期望信号的区域重构出干扰噪声协方差矩阵；然后推导了避免期望信号的导向矢量的估计值收敛到任一干扰的导向矢量或它们的线性组合的约束条件；进而以此约束条件和阵列输出功率最大化条件建立了期望信号导向矢量估计的优化问题，并使用凸优化软件估计出最优的期望信号导向矢量。讨论了该方法的计算复杂度并通过仿真实验验证了其有效性和优越性。仿真结果表明，当期望信号和干扰源存在随机指向误差和局部散射的情况下，所提方法在很大的输入信噪比范围内的输出信干噪比仍接近理论值，优于其他自适应波束形成方法。

Adaptive beamforming algorithm based on interference-noise covariance matrix reconstruction

In adaptive beamforming, the presence of the desired signal component in the training data, small sample size, and imprecise knowledge of the desired signal steering vector are the main causes of performance degradation. In order to solve this problem, this paper proposed a robust adaptive beamforming algorithm which performed interference-plus-noise covariance matrix reconstruction and desired signal steering vector estimation. In this algorithm, first the interference-plus-noise covariance matrix was reconstructed using Multiple Signal Classification (MUSIC) spatial spectrum in the signal-free angle section, then the constraint that prevented the convergence of the estimate of the desired signal steering vector to any of the interference steering vectors or their linear combination was derived, next this constraint was used together with the maximization of the array output power to formulate an optimization problem of estimating the desired signal steering vector, and convex optimization software was used to yield the desired signal steering vector. In the paper, the computational complexity of the proposed method was discussed and its effectiveness and superiority were validated by simulations. The simulation results demonstrate that the Signal to Interference plus Noise Ratio (SINR) of proposed adaptive beamformer is almost always close to optimal in a very large range of Signal-to-Noise Ratio (SNR) in the scenarios of random signal and interference look direction mismatch and incoherent local scattering, which is more robust than the existing beamformers.