系统工程与电子技术

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均匀线阵混合信源DOA估计与互耦误差自校正

景小荣1,2, 杨洋1, 张祖凡1,2, 陈前斌1,2   

  1. 1. 重庆邮电大学通信与信息工程学院, 重庆 400065;
    2. 重庆邮电大学移动通信技术重庆市重点实验室, 重庆 400065
  • 出版日期:2014-09-12 发布日期:2010-01-03

DOA estimation for mixed signals and mutual coupling self-calibration #br# for uniform linear array

JING Xiao-rong1,2, YANG Yang1, ZHANG Zu-fan1,2, CHEN Qian-bin1,2   

  1. 1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications,
    Chongqing 400065, China; 2. Chongqing Key Laboratory of Mobile Communications Technology,
    Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Online:2014-09-12 Published:2010-01-03

摘要:

针对均匀线阵(uniform linear array, ULA)互耦条件下混合信源的波达方向(direction of arrival, DOA)估计问题,基于联合对角化算法,提出了一种基于3步实现的DOA与互耦系数估计新算法。首先利用互耦矩阵的Toeplitz结构实现混合信源中独立信源的DOA及互耦系数的粗估计;然后结合斜投影及前后向空间平滑,实现混合信源DOA估计;最后以广义空间特征矩阵及混合信源DOA估计值为基础,提出一种非子空间类互耦系数自校正方法。计算机仿真结果表明,与同类算法相比,所提算法无论在DOA及互耦系数估计精度、还是在DOA估计成功率方面,均具有明显的优势,且对于高斯背景噪声具有普适性。

Abstract:

Based on the joint approximative diagonalization of eigen matrix, a novel algorithm which includes three steps is proposed to estimate the direction-of-arrival (DOA) of mixed signals and the mutual coupling coefficient of the uniform linear array (ULA) in presence of the mutual coupling error. Utilizing the Toeplitz structure of the mutual coupling matrix, the coarse estimates of the DOAs of the uncorrelated signals among  the mixed signals and mutual coupling coefficients are firstly obtained. Then the DOA estimates of the mixed signals are obtained based on the combination of the oblique projection with forward and backward spatial smoothing methods. Finally, a non-subspace method for mutual coupling self-calibration is presented by utilizing the estimates of the generalized spatial feature matrix and the estimated mixed signal DOAs. The computer simulation results indicate that the proposed algorithm has much better performance in DOAs and mutual coupling coefficient estimation and the successful rate compared with the similar algorithm, and it is also universal for Gaussian background noises.