FREQUENCY-DOMAIN IMPLEMENTATION OF GRIFFITHS-JIM ADAPTIVE BEAMFORMER
FREQUENCY-DOMAIN IMPLEMENTATION OF GRIFFITHS-JIM ADAPTIVE BEAMFORMER
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DOI：
10.1121/1.402825
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发表时间：
1992-06-01
影响因子：
2.4
通讯作者：
FANG, HD
中科院分区：
文献类型：
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作者：
CHEN, YH;FANG, HD
众所周知，Widrow [Proc. IEEE 63，719-720（1975）]提出的时域最小均方（LMS）算法在自适应滤波器应用中的缺点是，其收敛速度随着输入自相关矩阵的本征值扩展的增加而降低。然而，这个问题可以通过采用变换域LMS方法来克服[Narayan等人，IEEE Trans. ASSP 31，609-615（1983）]，其首先通过DFT或一些正交变换将输入时域信号变换成另一变换域信号，然后使用自正交化算法[Gitlin和Magee，IEEE Trans. Commun. 25，666-672（1977）]来优化自适应滤波器的可变权重。这种方法已被证明提供了很大的改善，在收敛速度的时域LMS方法。本文的目的是应用频域LMS算法与自正交化技术，称为FLMS，Griffiths-Jim自适应波束形成器，以加快自适应阵列信号的实时处理的收敛速度。结果表明，在时域和频域实现的波束形成器的两个最小均方误差是相同的。仿真结果表明，与LMS算法相比，FLMS算法具有更快的收敛速度和更好的调零干扰性能，特别是在特征值扩展较大的情况下。
It is well known that the drawback of the time-domain least-mean-square (LMS) algorithm proposed by Widrow [Proc. IEEE 63, 719-720 (1975)] in adaptive filter applications is that its convergence speed decreases as the eigenvalue spread of the input autocorrelation matrix increases. However, this problem can be overcome by employing the transform-domain LMS method [Narayan et al., IEEE Trans. ASSP 31, 609-615 (1983)] which first transforms input time-domain signals into another transform-domain signals through DFT or some orthogonal transforms and then uses the self-orthogonalizing algorithm [Gitlin and Magee, IEEE Trans. Commun. 25, 666-672 (1977)] to optimize the variable weights of an adaptive filter. This method has been shown to offer great improvement in convergence rate over the time-domain LMS method. The objective of this paper is to apply the frequency-domain LMS algorithm with the self-orthogonalizing technique, called FLMS, to the Griffiths-Jim adaptive beamformer to accelerate the convergence rate for real-time processing of adaptive array signals. It is shown that the two minimum mean-square errors of the beamformer implemented in the time and frequency domains are identical. Computer simulations show that the adaptive beamformer using the FLMS exhibits faster convergence behavior and better performance of nulling jammers than that using the LMS, especially for the larger eigenvalue spread.