一种基于快速迭代自适应算法的毫米波雷达波达角高精度测量方法

于丹阳; 杜磊

doi:10.12338/j.issn.2096-9015.2024.0198

一种基于快速迭代自适应算法的毫米波雷达波达角高精度测量方法

doi: 10.12338/j.issn.2096-9015.2024.0198

于丹阳,
杜磊^,

中国计量科学研究院，北京 100029

基金项目: 国家重点研发计划(2022YFF0604803、2017YFF0205006)；国家自然科学基金项目（62131001）。

详细信息

作者简介:
于丹阳（1999-），中国计量科学研究院在读研究生，研究方向：雷达目标参数校准，邮箱：yudanyang@nim.ac.cn

通讯作者:
杜磊（1982-），中国计量科学研究院副研究员，研究方向：运动学参数计量，邮箱：dulei@nim.ac.cn

计量
- 文章访问数: 103
- HTML全文浏览量: 25
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-22
- 录用日期: 2024-07-15
- 修回日期: 2024-07-20
- 网络出版日期: 2024-08-07

A High-Accuracy Measurement Method for Direction of Arrival of Millimeter-Wave Radar Based on Fast Iterative Adaptive Algorithm

YU Danyang,
DU Lei^,

National Institute of Metrology, Beijing 100029, China

摘要

摘要: 迭代自适应算法(Iterative Adaptive Algorithm, IAA)是一种超分辨算法，广泛用于毫米波雷达波达角(Direction Of Arrival, DOA)的高精度测量之中。然而，传统的IAA存在算法复杂、计算结果迟滞的问题，难以适用于对实时性要求较高的场景。此外，为了解决信源位置与网格字典不匹配而导致角度测量误差较大的问题，常采用网格细化的方法，这将进一步加剧IAA计算缓慢的问题。针对上述问题，提出了一种快速迭代自适应算法(Fast Iterative Adaptive Algorithm, FIAA)。FIAA采用粗细网格分次测量信源角度。首先在全空域内进行粗网格划分并使用IAA计算出真实信源的潜在区域，然后在信源潜在区域内进行细网格划分并更新信号方向矩阵，最后使用具有正则化协方差矩阵的IAA对信源角度进行高精度测量。实验结果表明， FIAA可以有效避免对非信源潜在区域的扫描与计算，计算耗时至少降低为IAA的4%，并在信噪比高于0dB时与IAA的计算精度基本一致，适用于高实时、高精度的毫米波雷达波达角测量场景之中。
- 计量学 /
- 毫米波雷达 /
- 迭代自适应 /
- 波达角测量 /
- 网格划分
Abstract: The Iterative Adaptive Algorithm (IAA) is a super-resolution algorithm widely applied for highly accurate measurement of the Direction of Arrival (DOA) in millimeter-wave radar. However, traditional IAA encounter challenges related to algorithmic complexity and computational delay, making them unsuitable for applications requiring real-time performance. Moreover, in order to reduce the angle estimation errors caused by the mismatch between the source locations and the grid dictionary, a common approach is to refine the grid. However, this further exacerbates the slow computation problem of the IAA. To tackle this problem, a Fast Iterative Adaptive Algorithm (FIAA) is proposed in this paper. The FIAA adopts a hierarchical grid refinement approach to iteratively measure source angles. Initially, a coarse grid division is implemented across the entire spatial domain and the potential areas of real source locations are identified by utilizing the IAA. Subsequently, within these potential regions, a refined grid division is executed, accompanied by updates to the signal direction matrix. Ultimately, the IAA, incorporating a regularized covariance matrix, is employed to attain highly accurate measurements of the signal source angles. Experimental results demonstrate that FIAA effectively circumvents scanning and computation in non-signal potential regions, the calculation time is reduced to at least 4% of the IAA, and the accuracy of the measurements are basically consistent with the IAA when the SNR is higher than 0dB. It is suitable for scenarios requiring high real-time performance and high-accuracy millimeter-wave radar DOA measurement.
- metrology /
- millimeter-wave radar /
- iterative adaptive algorithm /
- direction of arrival measurement /
- grid division

HTML全文

图 1 一维均匀天线阵示意图

Figure 1. Schematic diagram of a uniform linear antenna array

下载: 全尺寸图片幻灯片

图 2 FIAA流程图

Figure 2. Algorithm flowchart of FIAA

下载: 全尺寸图片幻灯片

图 3 增加天线个数时IAA与FIAA的计算耗时

Figure 3. Computational cost of IAA and FIAAs when increasing the number of antennas

下载: 全尺寸图片幻灯片

图 4 增加信源个数时IAA与FIAA的计算耗时

Figure 4. Computational cost of IAA and FIAAs when increasing the number of targets

下载: 全尺寸图片幻灯片

图 5 $ {\boldsymbol{K}}_{1} $与$ {\boldsymbol{K}}_{2} $变化时FIAA的计算耗时

Figure 5. Computational cost of FIAA when the $ {\boldsymbol{K}}_{1} $ and $ {\boldsymbol{K}}_{2} $ are changed

下载: 全尺寸图片幻灯片

图 6 信噪比变化时IAA与FIAA的均方根误差

Figure 6. RMSE of IAA and FIAAs when increasing the SNR

下载: 全尺寸图片幻灯片

表 1 网格细化时IAA和FIAA的计算耗时

Table 1. Computational cost of IAA and FIAAs when the mesh is refined

算法	网格精度为0.1°时的计算耗时/s	网格精度为0.05°时的计算耗时/s
IAA	0.386052	1.030609
FIAA	0.015321	0.016237
$ \dfrac{\mathrm{F}\mathrm{I}\mathrm{A}\mathrm{A}}{\mathrm{I}\mathrm{A}\mathrm{A}} $	3.97%	1.58%

下载: 导出CSV

参考文献(30)

[1]	黄岩, 张慧, 兰吕鸿康, 等. 汽车毫米波雷达信号处理技术综述[J]. 雷达学报, 2023, 12(5): 923-970.
[2]	袁静, 景冠军, 王建超, 等. 具有低噪声及高线性度的高性能MOCVD-SiNx/AlN/GaN毫米波MIS-HEMTs[J]. 红外与毫米波学报, 2024, 43(2): 200-206.
[3]	Sun Y L, Fei T, Li X B, et al. Real-time radar-based gesture detection and recognition built in an edge-computing platform[J]. IEEE Sensors Journal, 2020, 20(18): 10706-10716. doi: 10.1109/JSEN.2020.2994292
[4]	Gurbuz S Z, Amin M G. Radar-based human-motion recognition with deep learning: promising applications for indoor monitoring[J]. IEEE Signal Processing Magazine, 2019, 36(4): 16-28. doi: 10.1109/MSP.2018.2890128
[5]	Chen J B, Zhang D H, Wu Z, et al. Contactless electrocardiogram monitoring with millimeter wave radar[J]. IEEE Transactions on Mobile Computing, 2024, 23(1): 270-285. doi: 10.1109/TMC.2022.3214721
[6]	陈涛, 申梦雨, 史林, 等. 基于通道压缩的原子范数最小化DOA估计算法[J]. 仪器仪表学报, 2022, 43(4): 246-253.
[7]	王旭东, 仲倩, 闫贺, 等. 一种二维信号波达方向估计的改进多重信号分类算法[J]. 电子与信息学报, 2019, 41(9): 2137-2142. doi: 10.11999/JEIT181090
[8]	张泽宇. 毫米波MIMO雷达阵列优化设计与DOA估计方法研究[D]. 成都: 电子科技大学, 2023.
[9]	Dudek M, Nasr I, Bozsik G, et al. System analysis of a phased-array radar applying adaptive beam-control for future automotive safety applications[J]. IEEE Transactions on Vehicular Technology, 2015, 64(1): 34-47. doi: 10.1109/TVT.2014.2321175
[10]	舒月, 傅东宁, 陈展野, 等. 基于RD-ANM的毫米波雷达动目标超分辨DOA估计方法[J]. 雷达学报, 2023, 12(5): 986-999. doi: 10.12000/JR23040
[11]	单泽彪, 王宇祥, 常立民, 等. 冲击噪声背景下相干信号DOA估计[J]. 电子测量技术, 2022, 45(15): 166-171.
[12]	周英钢, 邵佳伟. 对数螺旋阵列的相干信号DOA估计研究[J]. 电子测量与仪器学报, 2023, 37(2): 220-227.
[13]	李燕, 何怡刚, 尹柏强. LFM信号DOA估计分数阶量纲归一化方法[J]. 电子测量与仪器学报, 2016, 30(3): 448-455.
[14]	王凌, 李国林, 李静, 等. 一种新的单次快拍二维ESPRIT算法[J]. 北京理工大学学报, 2013, 33(1): 99-104. doi: 10.3969/j.issn.1001-0645.2013.01.020
[15]	Yarbidi T, Li J, Stoica P, et al, Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 425-443.
[16]	王波, 刘德亮. 基于迭代自适应方法的近场源二维参数联合估计[J]. 计算机应用, 2019, 39(2): 523-527. doi: 10.11772/j.issn.1001-9081.2018061417
[17]	郝向阳, 韩晓东, 李晓明. 一种机载雷达双Capon迭代空时自适应处理算法[J]. 现代雷达, 2022, 44(12): 101-105.
[18]	黄以兰, 晋良念, 刘庆华. 一种车载毫米波FMCW MIMO雷达快速成像方法[J]. 雷达科学与技术, 2022, 20(2): 128-135,141. doi: 10.3969/j.issn.1672-2337.2022.02.002
[19]	Xue M, Xu L , Li J. IAA Spectral Estimation: Fast Implementation Using the Gohberg–Semencul Factorization[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3251-3261.
[20]	Glentis G O , Jakobsson A. Superfast Approximative Implementation of the IAA Spectral Estimate[J]. IEEE Transactions on Signal Processing, 2012, 60(1): 472-478.
[21]	Chen Y, Huang L T, So H C. Selective Range Iterative Adaptive Approach for High-Resolution DOA Estimation[J]. IEEE Access, 2019, 7: 15634-15640. doi: 10.1109/ACCESS.2019.2895365
[22]	揭允康, 张雯, 李想, 等. 一种基于迭代自适应的离网格DOA估计方法[J]. 电子与信息学报, 2023, 45(10): 3805-3811. doi: 10.11999/JEIT221061
[23]	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.
[24]	Gretsistas A, Plumbley M D. An alternating descent algorithm for the off-grid DOA estimation problem with sparsity constraints[C]. 2012 Proceedings of the 20^th European Signal Processing Conference, Bucharest, Romania, 2012.
[25]	Yang Z, XIE L H, Zhang C S. 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
[26]	Wu X H, Zhu W P, Yan J. 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
[27]	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
[28]	魏柱柱, 黄翔东, 解红岩. 基于迭代自适应方法的空间信号估计[J]. 计算机工程与应用, 2013, 49(S3): 360-363.
[29]	Stoica, Petre, Randolph L M. Spectral Analysis Of Signals[M]. Berlin: Springers, 2005.
[30]	Zhang Y , Yin Z , Huang Y , et al. Angular Super resolution for Scanning Radar with Improved Regularized Iterative Adaptive Approach[J]. IEEE Geoscience & Remote Sensing Letters, 2016, 13(6): 846-850.