电信科学 ›› 2015, Vol. 31 ›› Issue (2): 108-112.doi: 10.11959/j.issn.1000-0801.2015029

• 研究与开发 • 上一篇    下一篇

基于小波变换的RLS波束成形算法研究

赵季红1,2,雷佩1,王伟华2,曲桦2,贺丹1   

  1. 1 西安邮电大学通信与信息工程学院 西安 710061
    2 西安交通大学电子信息学院 西安 710049
  • 出版日期:2015-02-20 发布日期:2017-03-18
  • 基金资助:
    国家高技术研究发展计划（“863”计划）基金资助项目;国家高技术研究发展计划（“863”计划）基金资助项目;国家自然科学基金资助项目;国家科技重大专项“新一代宽带无线移动通信网”基金资助项目

RLS Beamforming Algorithm Based on Wavelet Transform

Jihong Zhao1,2,Pei Lei1,Weihua Wang2,Hua Qu2,Dan He1   

  1. 1 School of Telecommunication and Information Engineering, Xi'an University of Posts ＆Telecommunications, Xi'an 710061, China
    2 School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
  • Online:2015-02-20 Published:2017-03-18
  • Supported by:
    The National High Technology Research and Development Program of China （863 Program）;The National High Technology Research and Development Program of China （863 Program）;The National Natural Science Foundation of China;The National Science and Technology Major Project of China“A New Generation of Broadband Wireless Mobile Communication Network”

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摘要：

为了解决递归最小二乘算法（RLS）在较低信噪比（SNR）、遗忘因子较小的环境中，对噪声敏感、收敛时参数估计误差大的问题，引入小波变换去噪思想，提出了基于小波变换的RLS 波束成形算法。该算法利用小波变换软阈值法进行信号去噪，再采用RLS 算法进行波束成形。最后对实验进行仿真，仿真结果表明，与传统的 RLS 算法相比，该算法具有较小的稳态误差和较快的跟踪速度和收敛速度，并且波束成形效果好。

关键词: 波束成形, RLS算法, 小波变换

Abstract:

On the basis of analyzing the recursive least square algorithm（RLS）, in order to improve the performance of RLS algorithm, the idea of denoising by wavelet transform was introduced and the RLS beamforming algorithm which is based on wavelet transform was put forward. This algorithm uses soft thresholding wavelet transform for signal denoising, solves the problem that the traditional Adaptive beamforming algorithm is sensitive to noise and the steady-state error is big under the environment of the low signal to noise ratio（SNR）and small forgetting factor. Finally, the simulation results show that compared with the traditional RLS algorithm, this algorithm has smaller steady-state error and fast tracking speed and convergence speed, and good results of beamforming.

Key words: beamforming, recursive least square algorithm, wavelet transform

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