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针对大型阵列中自适应波束形成技术的实时性和鲁棒性问题,基于最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)波束形成的信号模型框架,提出一种通过对导向矢量进行处理以降低干扰的自适应波束形成算法——稳健联合迭代优化-导向自适应(Robust Joint Iterative Optimization-Direction Adaptive,RJIO-DA)算法。在联合迭代优化的基础上,将降维变换矩阵的每一个列向量看作独立的方向向量,引导子空间内每一个维度上的权值迭代,同时旋转导向向量,减小了由于导向误差的不确定性而导致的性能下降。仿真实验结果表明,与现有的降维算法相比,RJIO-DA算法计算复杂度低、收敛率高、鲁棒性好,可在期望方向上稳健地聚集波束,更好地形成干扰方向的自适应零陷。
Aimed at the real-time and robustness of adaptive beamforming in large array, this paper proposes a method based on the signal model framework of Minimum Variance Distortionless Response (MVDR) beamforming, Jamming adaptive beamforming algorithm - Robust Joint Iterative Optimization-Direction Adaptive (RJIO-DA) algorithm. Based on the joint iterative optimization, each column vector of the dimensionality reduction matrix is regarded as an independent directional vector, which guides the iteration of weights in each dimension in the subspace and rotates the steering vectors simultaneously, reducing the error of the steering error Uncertainty caused by the performance decline. Simulation results show that compared with the existing dimensionality reduction algorithms, the RJIO-DA algorithm has low computational complexity, high convergence rate and good robustness. It can robustly gather beams in a desired direction and form a better interference direction Adaptive zero sink.