In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a set of measured data. This is obtained from the first column and the first row of the sample covariance matrix (SCM) after averaging along its diagonals. Then, the power spectrum of the correlation sequence is estimated using the discrete Fourier transform (DFT). The DFT coefficients corresponding to the angles within the noise-plus-interference region are used to reconstruct the noise-plus-interference covariance matrix (NPICM), while the desired signal covariance matrix (DSCM) is estimated by identifying and removing the noise-plus-interference component from the SCM. In particular, the spatial power spectrum of the estimated received signal is utilized to compute the correlation sequence corresponding to the noise-plus-interference in which the dominant DFT coefficient of the noise-plus-interference is captured. A key advantage of the proposed adaptive beamforming is that only little prior information is required. Specifically, an imprecise knowledge of the array geometry and of the angular sectors in which the interferences are located is needed. Simulation results demonstrate that compared with previous reconstruction-based beamformers, the proposed approach can achieve better overall performance in the case of multiple mismatches over a very large range of input signal-to-noise ratios.
翻译:在本文中,基于从一组测量数据中重建随机过程的随机过程的自动关系序列(ACS)的构想,为适应性波束的形成提议了一个新颖和强大的算法。该算法来自按其对角平均之后的抽样共变矩阵的第一列和第一行。然后,使用离散的Fourier变换(DFT)来估计相关序列的功率频谱。与噪音加干涉区域内的角度相对应的DFT系数被用于重建噪音加干涉共变矩阵(NPICM),而想要的信号变异矩阵(DSCM)则通过从SCM中识别和删除噪音加互换部分来估计。特别是,所收到估计信号的空间功率频谱用于根据噪音加干涉(DFT)的互换(DFT)系数来计算相对应的关联序列。拟议的适应性调整的主要优点是,只需要很少的事先信息。具体地说,对阵列的信号变异性矩阵的变异性矩阵的精确性能是比重的模型,因此,在前方形结构上的模拟中可以取得较强的模拟的模拟式的模拟性能。