基于酉变换和稀疏贝叶斯学习的离格DOA估计

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

通信学报 ›› 2017, Vol. 38 ›› Issue (6): 177-182.doi: 10.11959/j.issn.1000-436x.2017049

• 学术通信 • 上一篇

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

上海大学通信与信息工程学院，上海 200444

修回日期:2017-04-23 出版日期:2017-06-25 发布日期:2017-06-30
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。
基金资助:
上海“东方学者”专项基金资助项目(B60-D107-14-201)

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

Yang GAO,Jun-li CHEN,Guang-li YANG

School of Communication Information Engineering,Shanghai University,Shanghai 200444,China

Revised:2017-04-23 Online:2017-06-25 Published:2017-06-30
Supported by:
Shanghai Easter-Scholar Professorship Award(B60-D107-14-201)

摘要/Abstract

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。

关键词: 到达角估计, 酉变换, 奇异值分解, 离格模型, 稀疏贝叶斯学习

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.

Key words: direction of arrival estimation, unitary transform, singular value decomposition, off-grid model, sparse Bayesian learning

中图分类号:

TN911.7

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.

图/表 5

参考文献 17

[1]	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.
[2]	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.
[3]	KRIM H , VIBERG M . Two decades of array signal processing research:the parametric approach[J]. IEEE Signal Processing Magazine, 1996,13(4): 67-94.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	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.

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

在线阅读

PDF下载

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 17

相关文章 12

Metrics

推荐阅读 0

[1]	郭兴林, 孙振晓, 周昱瑶, 漆莲芝, 张谊. 基于Pignistic概率转换和奇异值分解的证据冲突度量方法[J]. 通信学报, 2021, 42(4): 150-157.
[2]	吕新荣, 李有明, 国强. MIMO-OFDM系统的信道与脉冲噪声联合估计方法[J]. 通信学报, 2021, 42(12): 54-64.
[3]	侯进, 李昀喆, 李天宇. 基于去噪复数FastICA和稀疏重构的相干信号欠定DOA估计[J]. 通信学报, 2021, 42(11): 172-181.
[4]	王咸鹏,国月皓,黄梦醒,沈重,曹春杰,冯文龙. 互耦条件下MIMO雷达非圆目标稳健角度估计方法[J]. 通信学报, 2019, 40(7): 144-150.
[5]	曾勇,周灵杰,蒋忠元,刘志宏,马建峰. 基于奇异值分解的含权网络匿名化的安全性分析[J]. 通信学报, 2018, 39(5): 23-33.
[6]	吕新荣,李有明,余明宸. OFDM系统的信道与脉冲噪声的联合估计方法[J]. 通信学报, 2018, 39(3): 191-198.
[7]	申滨，王舒，黄琼，陈前斌. 基于Gerschgorin圆盘理论的认知无线电宽带频谱感知[J]. 通信学报, 2014, 35(4): 1-10.
[8]	申滨,王舒,黄琼,陈前斌. 基于Gerschgorin圆盘理论的认知无线电宽带频谱感知[J]. 通信学报, 2014, 35(4): 1-10.
[9]	朱翠涛,杨凡,汪汉新,李中捷. 基于因子图的分布式变分稀疏贝叶斯压缩感知[J]. 通信学报, 2014, 35(1): 140-147.
[10]	任啸天,徐晖,黄知涛,王丰华. 短码DS-SS信号扩频序列及信息序列联合盲估计方法[J]. 通信学报, 2012, 33(4): 169-175.
[11]	倪丽佳,王朔中,吴酋珉,裴蓓. 对局部内容篡改敏感的感知图像散列[J]. 通信学报, 2012, 33(11): 177-184.
[12]	张洪顺,许云林,湛江书. 基于信号子空间的ESPRIT-Like算法在相干DOA估计中的应用[J]. 通信学报, 2010, 31(7): 110-116.