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基于OFDM符号特征的干扰抑制与盲波束形成方法
引用本文:王磊,李广雪,李冬霞,刘海涛.基于OFDM符号特征的干扰抑制与盲波束形成方法[J].控制与决策,2020,35(6):1397-1402.
作者姓名:王磊  李广雪  李冬霞  刘海涛
作者单位:中国民航大学智能信号与图像处理天津市重点实验室,天津300300;中国民航大学智能信号与图像处理天津市重点实验室,天津300300;中国民航大学智能信号与图像处理天津市重点实验室,天津300300;中国民航大学智能信号与图像处理天津市重点实验室,天津300300
基金项目:国家自然科学基金民航联合基金项目(U1633108,U1733120);中央高校基本科研业务费专项基金项目(3122017012).
摘    要:为了抑制L波段数字航空通信系统1正交频分复用接收机中的测距机脉冲干扰信号的影响,提出基于正交频分复用符号特征的盲波束形成方法.阵列天线接收的数据首先经正交投影抑制测距机干扰,然后根据正交频分复用符号中循环前缀的延迟重复特征,将波束形成的权值求解问题转化为瑞利商的最大化问题,通过对延迟协方差矩阵的特征值分解获得阵列天线的波束形成权矢量,不需要预先知道期望信号的来向信息.仿真结果表明:所提出方法能够将波束主瓣对准期望信号来向,同时在干扰信号来向上形成深零陷,从而提高阵列输出端信噪比.所提出方法在低信噪比环境下波束形成性能优于内积最大化方法,随着信噪比增大,两种方法波束形成的性能逐渐接近.

关 键 词:L波段数字航空通信系统1  测距机  脉冲干扰  盲波束形成  循环前缀  正交频分复用

Interference suppression and blind beamforming based on OFDM symbol characteristic
WANG Lei,LI Guang-xue,LI Dong-xi,LIU Hai-tao.Interference suppression and blind beamforming based on OFDM symbol characteristic[J].Control and Decision,2020,35(6):1397-1402.
Authors:WANG Lei  LI Guang-xue  LI Dong-xi  LIU Hai-tao
Affiliation:Tianjin Key Laboratory for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China
Abstract:In order to suppress the distance measuring equipment (DME) impulse interference in the orthogonal frequency division multiplexing (OFDM) receiver of the L-band digital aviation communication system type 1 (L-DACS1), a blind beamforming method based on the OFDM symbol characteristics is proposed. The data received from the array antenna is firstly orthogonal projected to suppress the DME interference. Then, the interference-free data is used for beamforming. The weight of the beamforming is obtained by maximizing the Rayleigh quotient, using the delay repetition characteristics of the cyclic prefix for OFDM symbols, and no prior information, such as the direction of the desired OFDM signal, is required. Simulation results show that the proposed method can obtain a robust beam pattern, with the main lobe pointing to the direction of the OFDM signal. As a result, the signal-to-noise ratio (SNR) of the array output has been improved significantly. In addition, compared with the inner product maximization method, the proposed method performs better in low SNR environment. With the increase of the SNR, the performance difference between the two methods gradually decreases.
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