低信噪比条件下宽带欠定信号高精度DOA估计

doi:10.11999/JEIT160921

Abstract
Figure/Table
References (18)
Related Citation (15)

Download: PDF (785 KB)
Export: BibTeX | EndNote (RIS)

Abstract

In order to improve underdetermined wideband signals DOA estimation accuracy under low Signal to Noise Ratio (SNR) condition, an off-grid sparse learning via iterative minimization algorithm is proposed. Firstly, the novel algorithm vectorizes the covariance matrix in frequency domain to realize visual array extension, as a result, underdetermined wideband signals are transformed into overdetermined signals. Then linear transform is used to eliminate the noise contained virtual array elements, whitening process is utilized to the estimation error of covariance matrix, as a result, the interference in signals is suppressed. Finally, a Bayesian structure containing the joint sparsity parameter of different frequencies and off-grid parameter is built, the minimization sparse expressions of joint sparsity parameter and off-grid parameter are deduced and corresponding parameters are learned iteratively. Compared with other methods, the proposed method does not rely on any prior information, suppresses the inference in virtual array elements more efficiently, reduces the effects of off-grid problem, and gets higher DOA estimation accuracy and resolution under low SNR condition. Simulation experiments verify the validity of the novel algorithm.

Key words： Wideband signals DOA estimation Under low SNR condition Underdetermined signals Off-grid problem Sparse Learning via Iterative Minimization (SLIM)

Received: 12 September 2016 Published: 21 March 2017

PACS:

TN911.7

Fund:

The National Natural Science Foundation of China (61401504), The Military Plan of Scientific Research Project (2015XXX), The Natural Science Foundation of Hubei Province (2016CFB288)

Corresponding Authors: FENG Mingyue E-mail: fengmingyue2005@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	FENG Mingyue
	HE Minghao
	XU Jing
	LI Shaodong

Cite this article:

FENG Mingyue,HE Minghao,XU Jing等. High Accuracy DOA Estimation Under Low SNR Condition for Wideband Underdetermined Signals[J]. JEIT, 2017, 39(6): 1340-1347.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160921 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I6/1340

[1]	ZHANG Yong, HE Peiyu, and WANG Haijiang. Wideband coherent sources localization based on a two-node distributed sensor networks[J]. Signal Processing, 2016, 126(9): 103-110. doi: 10.1016/j.sigpro.2015.10.024.
[2]	ZHEN Jiaqi and WANG Zhifang. DOA estimation method for wideband signals by block sparse reconstruction[J]. Journal of Systems Engineering and Electronics, 2016, 27(1): 20-27. doi: 10.1109/JSEE.2016.00003.
[3]	朱立为, 汪亚, 王翔, 等. 空时频域中欠定混合条件下的波达方向估计[J]. 国防科技大学学报, 2015, 37(5): 149-154. doi: 10.11887/j.cn.201505023.
	ZHU Liwei, WANG Ya, WANG Xiang, et al. Underdetermined direction of arrival estimation based on spatial time-frequency distributions[J]. Journal of National University of Defense Technology, 2015, 37(5): 149-154. doi: 10.11887/j.cn.201505023.
[4]	HAN Keyong and NEHORAI Arye. Wideband Gaussian source processing using a linear nested array[J]. IEEE Signal Processing Letters, 2013, 20(11): 1110-1113. doi: 10.1109/LSP.2013.2281514.
[5]	SHEN Zhibo, DONG Chunxi, DONG Yangyang, et al. Broadband DOA estimation based on nested arrays[J]. International Journal of Antennas and Propagation, 2015, 2015(3): 1-7. doi: 10.1155/2015/974634.
[6]	SHEN Qing, LIU Wei, CUI Wei, et al. Low-complexity direction-of-arrival estimation based on wideband co-prime arrays[J]. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2015, 23(9): 1445-1453. doi: 10.1109/TASLP.2015.2436214.
[7]	CHI Yuejie, SCHARF L L, PEZESHKI A, et al. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2182-2196. doi: 10.1109/TSP.2011.2112650.
[8]	HE Zhenqing, SHI Zhiping, HUANG Lei, et al. Underdetermined DOA estimation for wideband signals using robust sparse covariance fitting[J]. IEEE Signal Processing Letters, 2015, 22(4): 435-439. doi: 10.1109/LSP.2014.2358084.
[9]	WANG Lu, ZHAO Lifan, BI Guoan, et al. Novel wideband DOA estimation based on sparse Bayesian learning with Dirichlet process priors[J]. IEEE Transactions on Signal Processing, 2016, 64(2): 275-289. doi: 10.1109/TSP.2015. 2481790.
[10]	PAN Yujian, TAI Ning, and YUAN Naichang. Wideband DOA estimation via sparse Bayesian learning over a Khatri-Rao dictionary[J]. Radioengineering, 2015, 24(2): 552-557. doi: 10.13164/re.2015.0552.
[11]	熊坤来, 刘章孟, 柳征, 等. 基于ＥＭ算法的宽带信号DOA估计及盲分离[J]. 电子学报, 2015, 43(10): 2028-2033. doi: 10.3969/j.issn.03722112.2015.10.022.
	XIONG Kunlai, LIU Zhangmeng, LIU Zheng, et al. Broadband DOA estimation and blind source separation based on EM algorithm[J]. Acta Electronica Sinica, 2015, 43(10): 2028-2033. doi: 10.3969/j.issn.03722112.2015.10.022.
[12]	XU Luzhou, ZHAO Kexin, LI Jian, et al. Wideband source localization using sparse learning via iterative minimization[J]. Signal Processing, 2013, 93(12): 3504-3514. doi: 10.1016/j.sigpro.2013.04.005.
[13]	TAN Xing, ROBERTS W, LI Jian, et al. Sparse learning via iterative minimization with application to MIMO radar imaging[J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1088-1101. doi: 10.1109/TSP.2010.2096218.
[14]	邓佳欣, 廖桂生, 杨志伟, 等. 基于虚拟孔径扩展的子带信息融合宽带DOA估计[J]. 系统工程与电子技术, 2016, 38(2): 245-250. doi: 10.3969/j.issn.1001-506X.2016.02.01.
	DENG Jiaxin, LIAO Guisheng, YANG Zhiwei, et al. Subband information fusion for wideband DOA estimation based on virtual array[J]. Systems Engineering and Electronics, 2016, 38(2): 245-250. doi: 10.3969/j.issn.1001- 506X.2016.02.01.
[15]	QIN Si, ZHANG Y D, and AMIN M G. Generalized coprime array configurations for direction-of-arrival estimation[J]. IEEE Transactions on Signal Processing, 2015, 63(6): 1377-1390. doi: 10.1109/TSP.2015.2393838.
[16]	VAIDYANATHAN P P and PAL Piya. Sparse sensing with co-prime samplers and arrays[J]. IEEE Transactions on Signal Processing, 2011, 59(2): 573-586. doi: 10.1109/TSP. 2010.2089682.
[17]	YANG Zai, XIE Lihua, and ZHANG Cishen. Off-Grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38-43. doi: 10.1109/TSP.2012.2222378.
[18]	ZHAO Yonghong, ZHANG Linrang, and GU Yabin. Array covariance matrix-based sparse Bayesian learning for off-grid direction-of-arrival estimation[J]. Electronics Letters, 2016, 52(5): 401-402. doi: 10.1049/el.2015.2931.