基于酉变换和稀疏贝叶斯学习的离格DOA估计

高阳; 陈俊丽; 杨广立

doi:10.11959/j.issn.1000-436x.2017049

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基于酉变换和稀疏贝叶斯学习的离格DOA估计

学术通信 | 更新时间：2024-06-05

- 基于酉变换和稀疏贝叶斯学习的离格DOA估计
- Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning
- 通信学报 2017年38卷第6期页码：177-182
- 作者机构：
  
  上海大学通信与信息工程学院，上海 200444
- 作者简介：
  
  [ "高阳（1992-），男，甘肃天水人，上海大学硕士生，主要研究方向为阵列信号处理和波达方向估计。" ]
  [ "陈俊丽（1972-），女，陕西西安人，博士，上海大学副教授，主要研究方向为子波分析和智能信息处理。" ]
  [ "杨广立（1974-），男，山西晋中人，博士，上海大学教授，主要研究方向为第五代通信的基站和终端的天线与射频技术、MIMO天线和系统信道容量。" ]
- 基金信息：
  
  上海“东方学者”专项基金资助项目(B60-D107-14-201)
- DOI：10.11959/j.issn.1000-436x.2017049
  中图分类号： TN911.7
- 网络出版日期：2017-06，
  
  纸质出版日期：2017-06-25
- 稿件说明：
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高阳, 陈俊丽, 杨广立. 基于酉变换和稀疏贝叶斯学习的离格DOA估计[J]. 通信学报, 2017,38(6):177-182.

Yang GAO, Jun-li CHEN, Guang-li YANG. Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning[J]. Journal on communications, 2017, 38(6): 177-182.
高阳, 陈俊丽, 杨广立. 基于酉变换和稀疏贝叶斯学习的离格DOA估计[J]. 通信学报, 2017,38(6):177-182. DOI： 10.11959/j.issn.1000-436x.2017049.

Yang GAO, Jun-li CHEN, Guang-li YANG. Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning[J]. Journal on communications, 2017, 38(6): 177-182. DOI： 10.11959/j.issn.1000-436x.2017049.

摘要

针对传统稀疏贝叶斯学习算法(SBL)在解决低信噪比条件下信号到达角(DOA)估计有效性的问题，提出基于酉变换的实数域稀疏贝叶斯学习(RV-OGSBL)的快速离格DOA估计方法。该方法首先对均匀线阵的实际接收信号通过构造增广矩阵作为 DOA 估计的处理信号，然后利用酉变换将估计模型从复数域转化到实数域，进一步在实数域下将离格模型与稀疏贝叶斯学习算法相结合迭代处理实现 DOA 估计，获得较高的估计精度。仿真结果表明，RV-OGSBL 方法不仅能保持传统 SBL 算法的性能，而且显著降低了计算复杂度。在低信噪比和低快拍数的情况下，算法运行时间降低约50%，表明该方法是一种快速的DOA估计算法。

Abstract

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation

against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.Then

estimation model was transformed from complex value to real value by using unitary transformation.In the next step

off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.The accuracy of estimation could made relatively high.The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm

but also reduces the computational complexity significantly.Under the situation of lower signal noise ratio (SNR) and low number of snapshots

the running time of algorithm is reduced about 50%.This shows the RV-OGSBL method is a rapid DOA estimation algorithm.

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references

CHAN A Y J , LITVA J . Music and maximum likelihood techniques on two-dimensional DOA estimation with uniform circular array [J ] . IEEE Proceedings - Radar,Sonar and Navigation , 1995 , 142 ( 3 ): 105 - 114 .

ROY R , KAILATH T . ESPRIT-estimation of signal parameters via rotational invariance techniques [J ] . IEEE Transactions on Acoustics Speech ＆Signal Processing , 1989 , 37 ( 7 ): 984 - 995 .

KRIM H , VIBERG M . Two decades of array signal processing research:the parametric approach [J ] . IEEE Signal Processing Magazine , 1996 , 13 ( 4 ): 67 - 94 .

MALIOUTOV D , CETIN M , WILLSKY A S . A sparse signal reconstruction perspective for source localization with sensor arrays [J ] . IEEE Transactions on Signal Processing , 2003 , 53 ( 8 ): 3010 - 3022 .

YIN J , CHEN T . Direction-of-arrival estimation using a sparse representation of array covariance vectors [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 9 ): 4489 - 4493 .

XU D , HU N , YE Z , et al . The estimate for DOAs of signals using sparse recovery method [C ] // IEEE International Conference on Acoustics . 2012 : 2573 - 2576 .

SHAGHAGHI M , VOROBYOV S A . An improved L1-SVD algorithm based on noise subspace for DOA estimation [J ] . Progress in Electromagnetics Research C , 2012 , 29 ( 12 ): 109 - 122 .

孙磊 , 王华力 , 许广杰 , 等 . 基于稀疏贝叶斯学习的高效 DOA 估计方法 [J ] . 电子与信息学报 , 2013 , 35 ( 5 ): 1196 - 1201 .

SUN L , WANG H L , XU G J , et al . Efficient direction-of-arrival estimation via sparse Bayesian learning [J ] . Journal of Electronics ＆Information Technology , 2013 , 35 ( 5 ): 1196 - 1201 .

LEI W , CHEN B . High-resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning [J ] . Electronics Letters , 2015 , 51 ( 3 ): 285 - 287 .

HUANG Q , ZHANG G , FANG Y . Real-valued DOA estimation for spherical arrays using sparse Bayesian learning [J ] . Signal Processing , 2016 , 125 ( C ): 79 - 86 .

WU X , ZHU W , YAN J . Direction of arrival estimation for off-grid signals based on sparse Bayesian learning [J ] . IEEE Sensors Journal , 2015 , 16 ( 7 ): 2004 - 2016 .

PAN Y , ZHU H , TAI N , et al . 2-D off-grid DOA estimation using sparse Bayesian learning with L-shape array [C ] // IEEE International Conference on Signal Processing,Communications and Computing . 2015 .

HUARNG K C , YEH C C . A unitary transformation method for angle-of-arrival estimation [J ] . IEEE Transactions on Signal Processing , 1991 , 39 ( 4 ): 975 - 977 .

YILMAZER N , KOH J , SARKAR T K . Utilization of a unitary transform for efficient computation in the matrix pencil method to find the direction of arrival [J ] . IEEE Transactions on Antennas ＆Propagation , 2006 , 54 ( 1 ): 175 - 181 .

YANG Z , XIE L , ZHANG C . Off-grid direction of arrival estimation using sparse Bayesian inference [J ] . IEEE Transactions on Signal Processing , 2011 , 61 ( 1 ): 38 - 43 .

LIANG Y , YING R , LU Z , et al . Off-grid direction of arrival estimation based on joint spatial sparsity for distributed sparse linear arrays [J ] . Sensors , 2014 , 14 ( 11 ):21981.

ZHAO Y , ZHANG L , GU Y . Array covariance matrix-based sparse Bayesian learning for off-grid direction-of-arrival estimation [J ] . Electronics Letters , 2016 , 52 ( 5 ): 401 - 402 .

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