一种基于迭代自适应的离网格DOA估计方法

揭允康; 张雯; 李想; 叶晓东; 王昊; 陶诗飞

doi:10.11999/JEIT221061

一种基于迭代自适应的离网格DOA估计方法

doi: 10.11999/JEIT221061

揭允康¹,
张雯¹,
李想²,
叶晓东¹,
王昊¹,
陶诗飞^1, ,

1.
南京理工大学电子工程与光电技术学院南京 210094
2.
北方电子设备研究所北京 100191

基金项目: 国家重点研发计划(2021YFB3502500)

详细信息

作者简介:
揭允康：男，博士生，研究方向为稀疏阵列信号处理、DOA估计

张雯：女，硕士生，研究方向为阵列信号处理

李想：女，助理研究员，研究方向为电子信号侦查技术

叶晓东：男，副教授，研究方向为通信理论与技术、随机信号理论与应用、信号获取与处理、现代信号处理

王昊：男，教授，研究方向为数字波束形成天线系统、多功能雷达天线系统、新型通信天线系统

陶诗飞：男，副研究员，研究方向为雷达目标特性分析、雷达成像及信号处理、计算电磁学

通讯作者:
陶诗飞　s.tao@njust.edu.cn

中图分类号: TN97
计量
- 文章访问数: 1120
- HTML全文浏览量: 598
- PDF下载量: 204
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-12
- 修回日期: 2022-12-22
- 录用日期: 2023-01-11
- 网络出版日期: 2023-01-14
- 刊出日期: 2023-10-31

An Off-Grid DOA Estimation Based on Iterative Adaptive Approach

1.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2.
Northern Institute of Electronic Equipment of China, Beijing 100191, China

Funds: The National Key Research and Development Program (2021YFB3502500)

摘要

摘要: 针对真实信源位置与字典网格不匹配导致波达角估计(DOA)误差过大的问题，该文提出一种基于修正迭代自适应(IAA)功率谱算法的离网格(Off-Grid)DOA估计方法(OGIAA)。该方法首先通过修正IAA方法得到信号功率谱，读出功率峰值的对应网格角度作为粗估计结果，再利用平方误差代价函数，将代价函数2阶泰勒展开并最小化得到初始偏移量，最后交替优化功率分量和偏移量，实现高精度的离网格DOA估计。理论分析和仿真结果表明，该方法实现过程简单，无正则化参数影响，能准确估计出偏移网格的信源角度，在高阵列自由度的非均匀阵列上也同样具备高估计精度。
- DOA估计 /
- 离网格 /
- 自适应迭代
Abstract: According to the problems that the mismatch between the real source location and the dictionary grid leads to the great error of Direction Of Arrival (DOA), an Off-Grid DOA estimation based on modified Iterative Adaptive Approach (IAA) is proposed (OGIAA). Firstly, the signal power spectrum obtained by the IAA is modified, and the corresponding grid angle of the peak power is read out as the coarse estimation results. Then, the square error cost function is expanded to the second order Taylor expansion and minimized to obtain the initial offset. Finally, the power component and offset are optimized alternately to achieve high precision off grid DOA estimation. Theoretical analysis and simulation results show that the implementation process of this method is simple, and it can accurately estimate the source angle of the migration grid without regularization parameters. It also has higher estimation accuracy on non-uniform arrays with more degrees of freedom.
- Direction Of Arrival (DOA) estimation /
- Off-Grid (OG) /
- Iterative Adaptive Approach (IAA)

HTML全文

图 1 不同参数条件下的估计正确率

下载: 全尺寸图片幻灯片

图 2 不同算法在空域网格上的幅度对比图

下载: 全尺寸图片幻灯片

图 3 不同快拍数和信噪比下的算法估计精度

下载: 全尺寸图片幻灯片

图 4 互质阵列结构

下载: 全尺寸图片幻灯片

图 5 互质虚拟阵估计结果

下载: 全尺寸图片幻灯片

图 6 不同快拍数和信噪比下的算法估计精度

下载: 全尺寸图片幻灯片

参考文献(22)

[1]	金坚, 谷源涛, 梅顺良. 压缩采样技术及其应用[J]. 电子与信息学报, 2010, 32(2): 470–475. doi: 10.3724/SP.J.1146.2009.00497 JIN Jian, GU Yuantao, and MEI Shunliang. An introduction to compressive sampling and its applications[J]. Journal of Electronics &Information Technology, 2010, 32(2): 470–475. doi: 10.3724/SP.J.1146.2009.00497
[2]	YANG Zai, LI Jian, STOICA P, et al. Chapter 11 - sparse methods for direction-of-arrival estimation[M]. CHELLAPPA R and THEODORIDIS S. Academic Press Library in Signal Processing, Volume 7. Academic Press, 2018: 509–581.
[3]	TIBSHIRANI R. Regression shrinkage and selection via the lasso: A retrospective[J]. Journal of the Royal Statistical Society:Series B, 2011, 73(3): 273–282. doi: 10.1111/j.1467-9868.2011.00771.x
[4]	LI Lixiang, FANG Yuan, LIU Liwei, et al. Overview of compressed sensing: Sensing model, reconstruction algorithm, and its applications[J]. Applied Sciences, 2020, 10(17): 5909. doi: 10.3390/app10175909
[5]	ZHANG Yongchao, LI Jie, LI Minghui, et al. Online sparse reconstruction for scanning radar using beam-updating q-SPICE[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 3503905. doi: 10.1109/LGRS.2021.3058404
[6]	何团, 唐波, 张玉. 基于加权SPICE的MIMO-STAP稀疏恢复算法[J]. 信号处理, 2019, 35(8): 1417–1424. doi: 10.16798/j.issn.1003-0530.2019.08.017 HE Tuan, TANG Bo, and ZHANG Yu. Sparse recovery algorithm of MIMO-STAP based on weighted SPICE[J]. Journal of Signal Processing, 2019, 35(8): 1417–1424. doi: 10.16798/j.issn.1003-0530.2019.08.017
[7]	WANG Hao, ZHANG Hong, and MA Qiming. Sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint[J]. Multidimensional Systems and Signal Processing, 2022, 33(3): 807–817. doi: 10.1007/s11045-021-00811-x
[8]	STOICA P and BABU P. Sparse estimation of spectral lines: Grid selection problems and their solutions[J]. IEEE Transactions on Signal Processing, 2012, 60(2): 962–967. doi: 10.1109/TSP.2011.2175222
[9]	徐文先, 高志奇, 徐伟, 等. 基于迭代自适应的字典校正空时自适应处理算法[J]. 信号处理, 2021, 37(11): 2216–2226. doi: 10.16798/j.issn.1003-0530.2021.11.024 XU Wenxian, GAO Zhiqi, XU Wei, et al. Space-time adaptive processing with dictionary calibration based on iterative adaptive approach[J]. Journal of Signal Processing, 2021, 37(11): 2216–2226. doi: 10.16798/j.issn.1003-0530.2021.11.024
[10]	AUSTIN C D, ASH J N, and MOSES R L. Dynamic dictionary algorithms for model order and parameter estimation[J]. IEEE Transactions on Signal Processing, 2013, 61(20): 5117–5130. doi: 10.1109/TSP.2013.2276428
[11]	GRETSISTAS A and PLUMBLEY M D. An alternating descent algorithm for the off-grid DOA estimation problem with sparsity constraints[C]. 2012 Proceedings of the 20th European Signal Processing Conference, Bucharest, Romania, 2012: 874–878.
[12]	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
[13]	WU Xiaohuan, ZHU Weiping, and YAN Jun. Direction of arrival estimation for off-grid signals based on sparse Bayesian learning[J]. IEEE Sensors Journal, 2016, 16(7): 2004–2016. doi: 10.1109/JSEN.2015.2508059
[14]	MA Yanan, CAO Xianbin, and WANG Xiangrong. Multi-source off-grid DOA estimation using iterative phase offset correction in coarray domain[J]. Digital Signal Processing, 2021, 112: 102998. doi: 10.1016/j.dsp.2021.102998
[15]	王洪雁, 于若男, 潘勉, 等. 基于协方差矩阵重构的离网格DOA估计方法[J]. 电子与信息学报, 2021, 43(10): 2863–2870. doi: 10.11999/JEIT200697 WANG Hongyan, YU Ruonan, PAN Mian, et al. Off-grid DOA estimation method based on covariance matrix reconstruction[J]. Journal of Electronics &Information Technology, 2021, 43(10): 2863–2870. doi: 10.11999/JEIT200697
[16]	YANG Zai and XIE Lihua. Enhancing sparsity and resolution via reweighted atomic norm minimization[J]. IEEE Transactions on Signal Processing, 2016, 64(4): 995–1006. doi: 10.1109/TSP.2015.2493987
[17]	CHUNG H, JOO J M, and KIM S Y. Off-grid DOA estimation on non-uniform linear array using constrained hermitian matrix[J]. Energies, 2020, 13(21): 5775. doi: 10.3390/en13215775
[18]	陈涛, 史林, 黄桂根, 等. 适用于任意几何结构平面阵列的无网格DOA估计算法[J]. 电子与信息学报, 2022, 44(3): 1052–1058. doi: 10.11999/JEIT210038 CHEN Tao, SHI Lin, HUANG Guigen, et al. Gridless DOA estimation algorithm for planar arrays with arbitrary geometry[J]. Journal of Electronics &Information Technology, 2022, 44(3): 1052–1058. doi: 10.11999/JEIT210038
[19]	ZHOU Chengwei, GU Yujie, SHI Zhiguo, et al. Off-grid direction-of-arrival estimation using coprime array interpolation[J]. IEEE Signal Processing Letters, 2018, 25(11): 1710–1714. doi: 10.1109/LSP.2018.2872400
[20]	ZHANG Xue, ZHENG Zhi, WANG Wenqin, et al. DOA estimation of coherent sources using coprime array via atomic norm minimization[J]. IEEE Signal Processing Letters, 2022, 29: 1312–1316. doi: 10.1109/LSP.2022.3179336
[21]	LI Jian and STOICA P. An adaptive filtering approach to spectral estimation and SAR imaging[J]. IEEE Transactions on Signal Processing, 1996, 44(6): 1469–1484. doi: 10.1109/78.506612
[22]	YARDIBI T, LI Jian, and STOICA P. Nonparametric and sparse signal representations in array processing via iterative adaptive approaches[C]. The 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2008: 278–282.