基于L型延迟阵列调制宽带转换器的信号载频和二维到达角联合估计

姜思仪; 付宁; 乔立岩; 彭喜元

doi:10.7498/aps.70.20201312

摘要

针对现有的基于欠采样的频率和二维到达角的联合估计存在结构复杂问题, 本文提出了一种基于调制宽带转换器技术的L型延迟阵列接收结构. 利用延迟通道与未延迟通道采样值之间的相位差可直接估计载频, 进而计算二维到达角, 无需额外的参数配对操作, 避免了配对步骤引入的误差和复杂度的提升. 并结合所提L型延迟阵列结构的特点构造相关矩阵和三线性模型, 提出了两种参数估计算法, 一种基于旋转不变子空间算法, 计算量小, 适用于需要实时处理的场景; 另一种基于正则分解技术, 鲁棒性较好, 适用于信噪比较低的应用场景. 仿真实验表明该方法能较好地从欠奈奎斯特样本中估计目标的载频和二维到达角参数.

关键词:

Abstract

As the signal spectrum in modern information technology becomes wider and wider, multi-band signals are distributed in a frequency range of tens of GHz. It covers a very wide spectrum but each RF signal has a very narrow band, and the distribution location of the band (or carrier frequency) is completely unknown. For the receiver, the single-band signals transmitted together constitute a multi-band signal. The sampling rate required to jointly estimate the space domain and frequency domain parameters of these signals is getting higher and higher. Modulated wideband converter system is an analog information conversion system for multiband analog signals, which is based on compressed sensing theory and greatly reduces the sampling rate. First, we propose an L-shaped delay array structure based on modulated wideband converter, which can estimate carrier frequency and two-dimensional arrival angles with a small number of samples. Secondly, two parameter-estimating algorithms are proposed based on the proposed structure. One is based on the estimating of signal parameter via rotational invariance technique (ESPRIT), which requires a small number of computations and is suitable for real-time processing application scenarios; the other algorithm is based on CANDECOMP/PARAFAC (CP) technique, which has better robustness and is suitable for applications with low signal-to-noise ratio. The samples of the delay channels can be directly used to estimate the carrier frequencies, and then the two-dimensional arrival angles are calculated. No additional pairing issue is required between the parameters. Then we give the time complexity analysis and space complexity analysis of the two methods. It can be found that the computational complexity and space storage occupation of the method based on ESPRIT are lower than those of the CP decomposition method. Then the conditions for unique parameter estimation are given. Finally, simulation experiments show that the proposed methods can estimate the carrier frequencies and two-dimensional arrival angles from sub-Nyquist samples. It can be found that the estimation method based on CP decomposition is more robust than the method based on ESPRIT, but at the cost of increased complexity of the algorithm.

Keywords:

compressed sensing /
joint estimation of array parameters /
two-dimensional direction of arrival /
array modulated wideband converter structure

作者及机构信息

哈尔滨工业大学电子与信息工程学院, 哈尔滨　150001

通信作者: 付宁, funinghit@163.com

基金项目: 国家自然科学基金(批准号: 62071149, 61671177)资助的课题

Authors and contacts

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Corresponding author: Fu Ning, funinghit@163.com

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62071149, 61671177)

文章全文

参考文献

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[2]	谢磊, 孙超, 刘雄厚, 蒋光禹 2016 物理学报 65 144303 Google Scholar Xie L, Sun C, Liu X H, Jiang G Y 2016 Acta Phys. Sin. 65 144303 Google Scholar
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[18]	Liu L, Gu J F, Wei P 2019 Signal Process. 154 87 Google Scholar
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[20]	Sidiropoulos N D, Giannakis G B, Bro R 2000 IEEE Trans. Signal Process. 48 810 Google Scholar
[21]	Liu L, Wei P 2017 IET Radar Sonar Navigation 11 1798 Google Scholar
[22]	Liu L, Wei P 2016 IEEE Signal Process. Lett. 25 1285 Google Scholar
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[24]	Stein S, Yair O, Cohen D, Eldar Y C 2017 IEEE Trans. Signal Process. 65 2645 Google Scholar
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施引文献

图 1 L型阵列MWC结构图

Fig. 1. L shaped array MWC.

下载: 全尺寸图片幻灯片

图 2 x轴延迟通道结构图

Fig. 2. x-axis delay channel.

下载: 全尺寸图片幻灯片

图 3 接收信号频谱示意图

Fig. 3. Spectrum of received signal.

下载: 全尺寸图片幻灯片

图 4 不同方法在M = 3, Q = 160, 迭代次数I = 50时的复杂度对比图

Fig. 4. Multiplications comparison vs. N with M = 3, Q = 160, and I = 50.

下载: 全尺寸图片幻灯片

图 5 不同阵元个数下参数估计效果　(a) 载频估计效果; (b) 方位角估计效果; (c) 俯仰角估计效果

Fig. 5. Performance of estimated parameters under different N: (a) Performance of carrier frequency; (b) performance of azimuth angle; (c) performance of elevation angle.

下载: 全尺寸图片幻灯片

图 6 不同快拍数Q下参数估计效果　(a) 载频估计效果; (b) 方位角估计效果; (c) 俯仰角估计效果

Fig. 6. Performance of estimated parameters under different$Q$: (a) Performance of carrier frequency; (b) performance of azimuth angle; (c) performance of elevation angle.

下载: 全尺寸图片幻灯片

图 7 不同信噪比下参数估计效果　(a) 载频估计效果; (b) 方位角估计效果; (c) 俯仰角估计效果

Fig. 7. Performance of estimated parameters under different SNR: (a) Performance of carrier frequency; (b) performance of azimuth angle; (c) performance of elevation angle.

下载: 全尺寸图片幻灯片

表 1 不同方法的复杂度对比

Table 1. Complexity comparison of different methods.

	基于ESPRIT的方法	基于CP分解的方法	CS-OMP方法^[28]
时间复杂度	$O({N^3} + {N^2}Q + {M^2})$	$O(IM \cdot {N^3} + IN{M^2} + {N^2}Q)$	$O(N{M^2}{P^2} + {N^3} + {N^2}(M + Q))$
空间复杂度	$O({N^2} + {M^2})$	$O({N^3})$	$O(N{P^2} + {N^2} + NM)$

下载: 导出CSV

[1]	Hassanien A, Vorobyov S A 2011 IEEE Trans. Signal Process. 59 2669 Google Scholar
[2]	谢磊, 孙超, 刘雄厚, 蒋光禹 2016 物理学报 65 144303 Google Scholar Xie L, Sun C, Liu X H, Jiang G Y 2016 Acta Phys. Sin. 65 144303 Google Scholar
[3]	孙梅, 周士弘 2016 物理学报 65 164302 Google Scholar Sun M, Zhou S H 2016 Acta Phys. Sin. 65 164302 Google Scholar
[4]	Yang C, Xia B, Xie W, Huang K, Yao Y, Zhao Y 2018 IEEE Trans. Veh. Technol. 67 842 Google Scholar
[5]	巴斌, 刘国春, 李韬, 林禹丞, 王瑜 2015 物理学报 64 078403 Google Scholar Ba B, Liu G C, Li T, Lin Y C, Wang Y 2015 Acta Phys. Sin. 64 078403 Google Scholar
[6]	Donoho D L 2006 IEEE Trans. Inf. Theory 52 1289 Google Scholar
[7]	Candes E J, Wakin M B 2008 IEEE Signal Process. Mag. 25 21 Google Scholar
[8]	康志伟, 吴春艳, 刘劲, 马辛, 桂明臻 2018 物理学报 67 099701 Google Scholar Kang Z W, Wu C Y, Liu J, Ma X, Gui M Z 2018 Acta Phys. Sin. 67 099701 Google Scholar
[9]	冷雪冬, 王大鸣, 巴斌, 王建辉 2017 物理学报 66 090703 Google Scholar Leng X D, Wang D M, Bang B, Wang J H 2017 Acta Phys. Sin. 66 090703 Google Scholar
[10]	Zhao Y, Hu Y H, Wang H 2012 IEEE Trans. Instrum. Meas. 61 579 Google Scholar
[11]	Tropp J A, Wakin M B, Duarte M F, et al. 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing Toulouse, France, May 14–19, 2006 p873
[12]	Kirolos S, Laska J, Wakin M, et al. 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software, Richardson, Texas, USA, October 29–30, 2006 p71
[13]	Mishali M, Eldar Y C 2009 IEEE Trans. Signal Process. 57 993 Google Scholar
[14]	Mishali M, Eldar Y C 2010 IEEE J. Sel. Topics Signal Process. 4 375 Google Scholar
[15]	Mishali M, Eldar Y C, Dounaevsky O, Shoshan E 2009 IET Circ. Device. Syst. 5 8 Google Scholar
[16]	黄翔东, 刘明卓, 杨琳, 刘琨, 刘铁根 2017 物理学报 66 188401 Google Scholar Huang X D, Liu M Z, Yang L, Liu K, Liu T G 2017 Acta Phys. Sin. 66 188401 Google Scholar
[17]	沈志博, 赵国庆, 董春曦, 黄龙 2014 航空学报 35 1357 Google Scholar Shen Z B, Zhao G Q, Dong C X, Huang L 2014 Acta Aeronaut. Astronaut. Sin. 35 1357 Google Scholar
[18]	Liu L, Gu J F, Wei P 2019 Signal Process. 154 87 Google Scholar
[19]	Liu L, Wei P, Zhang H G 2017 IEEE International Conference on Computer and Communications, Chengdu, China, December 13–16, 2017 p843
[20]	Sidiropoulos N D, Giannakis G B, Bro R 2000 IEEE Trans. Signal Process. 48 810 Google Scholar
[21]	Liu L, Wei P 2017 IET Radar Sonar Navigation 11 1798 Google Scholar
[22]	Liu L, Wei P 2016 IEEE Signal Process. Lett. 25 1285 Google Scholar
[23]	Stein S, Yair O, Cohen D, Eldar Y C 2015 IEEE International Workshop on Signal Processing Advances in Wireless Communications Stockholm, Sweden, June 28–July 1, 2015 p331
[24]	Stein S, Yair O, Cohen D, Eldar Y C 2017 IEEE Trans. Signal Process. 65 2645 Google Scholar
[25]	Cui C, Wu W, Wang W Q 2017 IEEE Sensors J. 17 7470 Google Scholar
[26]	Chen T, Liu L Z, Guo L M 2018 IET Radar, Sonar Navigation 12 873 Google Scholar
[27]	陈玉龙, 黄登山 2012 计算机工程与应用 48 159 Google Scholar Chen Y L, Huang D S 2012 Computer Engineering and Applications 48 159 Google Scholar
[28]	Esmaeil R, Farzan S M, Mohammad S S 2018 IET Radar, Sonar Navigation 12 889 Google Scholar

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