基于MIAA的稀疏轨迹扫描毫米波三维成像算法

doi:10.12305/j.issn.1001-506X.2025.11.10

摘要/Abstract

摘要：

采用稀疏轨迹扫描可以显著减少合成孔径雷达毫米波三维成像扫描时间，但会导致整行或整列的回波数据大量缺失，从而降低成像质量。针对此问题，提出一种基于缺失数据迭代自适应(missing-data iterative adaptive approach, MIAA)的稀疏轨迹扫描毫米波三维成像算法。首先，分析单一扫描方向上雷达回波信号在方位压缩后的稀疏特性；然后，将方位压缩过程表征为稀疏基矩阵并嵌入MIAA算法，对缺失数据矩阵进行逐行或逐列恢复；最后，对补全的数据进行三维成像。实验结果表明，对于稀疏轨迹扫描得到的30%全采样回波数据，所提算法与现有算法相比具有更好的成像效果。

关键词: 毫米波雷达, 三维成像, 稀疏成像, 迭代自适应

Abstract:

The use of sparse trajectory scanning can significantly reduce the scanning time of synthetic aperture radar millimeter-wave three-dimensional imaging, yet it can cause the absence of a large amount of data for the entire row or column, resulting in degradation of imaging quality. To address this problem, a millimeter wave sparse trajectory scanning three-dimensional imaging algorithm based on missing-data iterative adaptive approach （MIAA） is proposed. Firstly, the sparse characteristic of radar echo signal in a single scanning direction after azimuth compression is analyzed. Subsequently, the azimuth compression process is characterised as a sparse basis matrix embedded in MIAA algorithm, which recovers the missing-data matrix row-by-row or column-by-column. Finally, the complete data is used to reconstruct three-dimensional image. The experimental results show that up to 30% of fully sampled data obtained from sparse trajectory scanning, the proposed algorithm achieves better imaging results than existing algorithms.

Key words: millimeter-wave radar, three-dimensional imaging, sparse imaging, iterative adaptive approach

中图分类号:

TN 957

房瑞祥, 石晓进, 张云华. 基于MIAA的稀疏轨迹扫描毫米波三维成像算法[J]. 系统工程与电子技术, 2025, 47(11): 3612-3625.

Ruixiang FANG, Xiaojin SHI, Yunhua ZHANG. MIAA based three-dimensional millimeter-wave imaging algorithm with sparse trajectory scanning[J]. Systems Engineering and Electronics, 2025, 47(11): 3612-3625.

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参考文献 31

1	JIANG M J, XU G, PEI H, et al. 4D high-resolution imagery of point clouds for automotive mmWave radar[J]. IEEE Trans. on Intelligent Transportation Systems, 2024, 25 (1): 998- 1012.
2	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.
	HUANG Y, ZHANG H, LAN L H K, et al. Overview of signal processing techniques for automotive millimeter-wave radar[J]. Journal of Radars, 2023, 12 (5): 923- 970.
3	ZHANG F L, ZHANG Z B, KANG L, et al. mmTAA: a contact-less thoracoabdominal asynchrony measurement system based on mmWave sensing[J]. IEEE Trans. on Mobile Computing, 2025, 24 (2): 627- 641.
4	MUPPALA A V, SARABANDI K. Low-cost 3-D millimeter-wave concealed weapons detection using single transceiver affine synthetic arrays[J]. IEEE Trans. on Microwave Theory and Techniques, 2023, 71 (12): 5445- 5456.
5	LI S C, WU S Y. Low-cost millimeter wave frequency scanning based synthesis aperture imaging system for concealed weapon detection[J]. IEEE Trans. on Microwave Theory and Techniques, 2022, 70 (7): 3688- 3699.
6	WINGREN N, SJÖBERG D. Nondestructive testing using mm-wave sparse imaging verified for singly curved composite panels[J]. IEEE Trans. on Antennas and Propagation, 2023, 71 (1): 1185- 1189.
7	OCH A, HÖLZL P A, SCHUSTER S, et al. High-resolution millimeter-wave tomography system for nondestructive testing of low-permittivity materials[J]. IEEE Trans. on Microwave Theory and Techniques, 2021, 69 (1): 1105- 1113.
8	BEVACQUA M T, DI MEO S, CROCCO L, et al. Millimeter-waves breast cancer imaging via inverse scattering techniques[J]. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2021, 5 (3): 246- 253.
9	VAKALIS S, CHEN D, NANZER J A. Millimeter-wave imaging at 652 frames per second[J]. IEEE Journal of Microwaves, 2021, 1 (3): 738- 746.
10	WANG S G, LI S Y, HOORFAR A, et al. Compressive sensing-based sparse MIMO array synthesis for wideband near-field millimeter-wave imaging[J]. IEEE Trans. on Aerospace and Electronic Systems, 2023, 59 (6): 7681- 7697.
11	LIN B, LI C, JI Y C, et al. Efficient scaling techniques for 2-D sparse MIMO array far-field imaging[J]. IEEE Sensors Journal, 2024, 24 (8): 12604- 12615.
12	TU H, YU L B, WANG Z L, et al. 3-D millimeter-wave imaging for sparse MIMO array with range migration and l₂-norm-reinforced sparse bayesian learning[J]. IEEE Trans. on Instrumentation and Measurement, 2025, 74
13	YANIK M E, WANG D, TORLAK M. Development and demonstration of MIMO-SAR mmWave imaging testbeds[J]. IEEE Access, 2020, 8, 126019- 126038.
14	CHEN X, YANG Q, WANG H Q, et al. Adaptive ADMM-based high-quality fast imaging algorithm for short-range MMW MIMO-SAR systems[J]. IEEE Trans. on Antennas and Propagation, 2023, 71 (11): 8925- 8935.
15	LI Y D, ZHANG D H, GENG R X, et al. IFNet: deep imaging and focusing for handheld SAR with millimeter-wave signals[J]. IEEE Trans. on Mobile Computing, 2025, 24 (3): 2166- 2180.
16	LAVIADA J, ÁLVAREZ-NARCIANDI G, LAS-HERAS F. Artifact mitigation for high-resolution near-field SAR images by means of conditional generative adversarial networks[J]. IEEE Trans. on Instrumentation and Measurement, 2022, 71
17	杨磊, 霍鑫, 申瑞阳, 等. 可信推断近场稀疏综合阵列三维毫米波成像[J]. 雷达学报, 2024, 13 (5): 1092- 1108.
	YANG L, HUO X, SHEN R Y, et al. Credible inference of near-field sparse array synthesis for three-dimensional millimeter-wave imagery[J]. Journal of Radars, 2024, 13 (5): 1092- 1108.
18	DONOHO D L. Compressed sensing[J]. IEEE Trans. on Information Theory, 2006, 52 (4): 1289- 1306.
19	LI S Y, ZHAO G Q, SUN H J, et al. Compressive sensing imaging of 3-D object by a holographic algorithm[J]. IEEE Trans. on Antennas and Propagation, 2018, 66 (12): 7295- 7304.
20	王谋, 韦顺军, 沈蓉, 等. 基于自学习稀疏先验的三维SAR成像方法[J]. 雷达学报, 2023, 12 (1): 36- 52.
	WANG M, WEI S J, SHEN R, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12 (1): 36- 52.
21	CANDES E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25 (2): 21- 30.
22	YARDIBI T, LI J, STOICA P, et al. Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46 (1): 425- 443.
23	STOICA P, LI J, LING J, et al. Missing data recovery via a nonparametric iterative adaptive approach[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2009: 3369−3372.
24	WANG X Y, WANG R, DENG Y K, et al. SAR signal recovery and reconstruction in staggered mode with low oversampling factors[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15 (5): 704- 708.
25	SHEEN D M, MCMAKIN D L, HALL T E. Three-dimensional millimeter-wave imaging for concealed weapon detection[J]. IEEE Trans. on Microwave Theory and Techniques, 2001, 49 (9): 1581- 1592.
26	MENG Y, LIN C, QING A Y, et al. Accelerated holographic imaging with range stacking for linear frequency modulation radar[J]. IEEE Trans. on Microwave Theory and Techniques, 2022, 70 (3): 1630- 1638.
27	CUMMING I G, WONG F. 合成孔径雷达成像算法与实现[M]. 洪文, 胡东辉, 韩冰, 译. 北京: 电子工业出版社, 2019.
	CUMMING I G, WONG F. Digital processing of synthetic aperture radar data: algorithms and implementation[M]. HONG W, HU D H, HAN B, trans. Beijing: Publishing House of Electronics Industry, 2019.
28	YANIK M E, TORLAK M. Near-field MIMO-SAR millimeter-wave imaging with sparsely sampled aperture data[J]. IEEE Access, 2019, 7, 31801- 31819.
29	保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.
	BAO Z, XING M D, WANG T. Radar imaging technology[M]. Beijing: Publishing House of Electronics Industry, 2005.
30	BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2 (1): 183- 202.
31	WEI S J, ZHOU Z C, WANG M, et al. 3DRIED: a high-resolution 3-D millimeter-wave radar dataset dedicated to imaging and evaluation[J]. Remote Sensing, 2021, 13 (17): 3366.

参数名称	数值
载波频率/GHz	77
信号带宽/GHz	4
水平向合成孔径/mm	200
垂直向合成孔径/mm	200
水平向采样间隔/mm	2
垂直向采样间隔/mm	2

成像方法	PSLR	ISLR
全采样-RMA	−9.950	−17.188
稀疏采样-RMA	−5.071	1.369
FT-MIAA	−5.090	1.373
FSR-MIAA	−9.187	−6.901

VSSR	算法	PSNR/dB	SSIM
50%	RMA	17.128	0.532
	CS-FISTA	20.830	0.654
	FT-MIAA	28.287	0.727
	FSR-MIAA	31.493	0.847
30%	RMA	15.180	0.474
	CS-FISTA	20.506	0.589
	FT-MIAA	23.741	0.598
	FSR-MIAA	27.695	0.796

目标	算法	PSNR/dB	SSIM
扳手	RMA	22.861	0.564
	CS-FISTA	27.149	0.721
	FT-MIAA	25.375	0.432
	FSR-MIAA	34.776	0.844
匕首	RMA	23.386	0.521
	CS-FISTA	27.547	0.751
	FT-MIAA	24.204	0.406
	FSR-MIAA	35.646	0.834

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