Open AccessAn improved algorithm for radar adaptive beamforming based on machine learning
Author(s) -
Moyu Bai,
Hao Líu,
Haochuan Chen,
Shengming Gu,
Zhenhua Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012114
Subject(s) - computer science , adaptive beamformer , algorithm , least mean squares filter , radar , beamforming , stability (learning theory) , rate of convergence , adaptive filter , convergence (economics) , noise (video) , key (lock) , artificial intelligence , machine learning , telecommunications , computer security , economics , economic growth , image (mathematics)
In the field of radar digital signal processing, adaptive beamforming is a widely used technique for suppressing interference and noise. The Least Mean Square Algorithm (LMS) is a simple and easy algorithm for adaptive digital beamforming. However, it has the disadvantage of not achieving a balance between convergence speed and stability. In order to improve the performance of adaptive beamforming, this paper firstly reviews the classical LMS algorithm and then the machine learning optimization algorithm. Improvement effects of the three machine learning methods on the LMS algorithm are analyzed. The results show that the improved LMS algorithm based on AdaGrad exhibits the best performance. The algorithm can independently adjust the adaptive learning rate of different parameter components, making the iterative process of the adaptive beamforming more stable, efficient, and suitable for both theoretical research and engineering practice.