低信噪比低采样点波束形成算法的研究与改进

Han Zhang, Li Cheng, Yang Li
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引用次数: 0

摘要

当输入信号的功率较小且系统中存在噪声和干扰成分时,会出现低信噪比,影响整个系统的输出。特别是在波束形成与特征分解相结合的情况下,由于矩阵分解得到的信号和子空间存在误差,导致两种方法得到的转向矢量不匹配。此外,由于采样数据和期望信号成分的误差、采样点的数量有限以及阵列的干扰,会严重降低波束形成器的性能。在转向矢量估计方法的基础上,提出了一种新的波束形成算法,有效地解决了子空间误差导致的转向矢量失配问题。首先,算法估计采样协方差矩阵。然后,提取干扰转向向量,以离散方式重构干扰加噪声协方差矩阵。最后,利用最大相关系数对感兴趣信号的转向向量进行优化。仿真结果表明,在低信噪比和低采样点数条件下,该波束形成器的性能优于大多数经典算法。当存在阵列摄动时,波束形成器鲁棒性高,实用性强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Improvement of Beamforming Algorithm for Low Signal-to-Noise Ratio and Low Sampling Points
When the power of the input signal is small and there are noise and interference components in the system, a low signal-to-noise ratio will occur, affecting the output of the entire system. Especially when beamforming is used in combination with eigen decomposition, the signal and the subspace obtained by matrix decomposition has errors, resulting in mismatch of the steering vectors obtained by such methods. In addition, the performance the beamformer will be seriously degraded due to the error of the sampled data and the components of the desired signal, the limited number of sampling points, and the disturbance of the array. Based on the method of steering vector estimation, this paper proposes a new beamforming algorithm, which can effectively solve the problem of steering vector mismatch caused by subspace errors. First, the algorithm estimates the sampling covariance matrix. Then, extracting the interference steering vector to reconstruct the interference plus noise covariance matrix in a discrete manner. Finally, using the maximum correlation coefficient to optimize the steering vector of the signal of interest. The simulation results show that the performance of the beamformer is better than that of most classical algorithms under the condition of low signal-to-noise ratio and low number of sampling points. When there is array perturbation, the beamformer has high robustness and strong practicability.
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