基于MN-MEA算法的无人机载SAR相位误差补偿处理

doi:10.3969/j.issn.1001-506X.2020.09.09

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (9): 1945-1952.doi: 10.3969/j.issn.1001-506X.2020.09.09

基于MN-MEA算法的无人机载SAR相位误差补偿处理

马彦恒¹(), 侯建强¹(), 张炜民²(), 李根¹()

1. 陆军工程大学石家庄校区无人机工程系, 河北石家庄, 0500032
2. 中国人民解放军73676部队, 江苏江阴, 214400

收稿日期:2019-10-21 出版日期:2020-08-26 发布日期:2020-08-26
作者简介:马彦恒 (1968-),男,教授,博士研究生导师，博士,主要研究方向为无人机信息获取与处理技术。E-mail:houtougao@sina.com|侯建强 (1990-),男,博士研究生,主要研究方向为无人机载机动SAR成像。E-mail:877784427@qq.com|张炜民 (1990-),男,工程师，主要研究方向为无人机载机动SAR成像。E-mail:402645157@qq.com|李根 (1991-),男,博士研究生,主要研究方向为无人机载机动SAR成像。E-mail:2321979326@qq.com
基金资助:
军内科研项目；双重实验室建设项目资助课题

Phase error compensation processing of UAV-borne SAR based on MN-MEA algorithm

Yanheng MA¹(), Jianqiang HOU¹(), Weimin ZHANG²(), Gen LI¹()

1. Department of Unmanned Aerial Vehicle, Army Engineering University, Shijiazhuang 050003, China
2. Unit 73676 of the PLA, Jiangyin 214400, China

Received:2019-10-21 Online:2020-08-26 Published:2020-08-26
Supported by:
军内科研项目；双重实验室建设项目资助课题

摘要/Abstract

摘要：

无人机载合成孔径雷达(synthetic aperture radar, SAR),在成像中更容易受到运动误差的干扰,造成图像质量下降。利用三维空间坐标系的斜距方程分离,可以为无人机载SAR成像提供更多的信息，但该方法并未考虑无人机载SAR成像中的运动误差补偿问题。针对无人机载机动SAR成像的相位误差补偿问题,进行相关研究。结合子图像划分,提出了基于迭代分块处理和误差相位初值模型的改进牛顿最小熵(modified Newton minimum entropy, MN-MEA)相位误差补偿算法,进一步校正了残余空变性误差和运动误差,改善成像质量。

关键词: 无人机, 合成孔径雷达, 相位误差, 最小熵

Abstract:

Unmanned aerial vehicle (UAV) borne synthetic aperture radar (SAR) is more susceptible to motion errors in imaging, resulting in image quality degradation. More information can be provided for SAR imaging by using the separation of slant distance equations in the 3-D coordinate system. However, this method does not consider the problem of motion error compensation in UAV SAR imaging. In this paper, the phase error compensation of UAV mobile SAR imaging is studied. Combined with the sub-image segmentation, an improved modified Newton minimum entropy (MN-MEA) phase error compensation algorithm based on iterative block processing and the initial phase error model is proposed, which further corrects the residual spatiality error and motion error and improves the imaging quality.

Key words: unmanned aerial vehicle (UAV), synthetic aperture radar (SAR), phase error, minimum entropy

中图分类号:

TN95

马彦恒, 侯建强, 张炜民, 李根. 基于MN-MEA算法的无人机载SAR相位误差补偿处理[J]. 系统工程与电子技术, 2020, 42(9): 1945-1952.

Yanheng MA, Jianqiang HOU, Weimin ZHANG, Gen LI. Phase error compensation processing of UAV-borne SAR based on MN-MEA algorithm[J]. Systems Engineering and Electronics, 2020, 42(9): 1945-1952.

图/表 5

参考文献 25

1	HOU J Q , MA Y H , LI G . A third-order range separation imaging algorithm for manoeuvring platform SAR[J]. Remote Sen-sing Letters, 2019, 10 (8): 786- 795.
2	CAO P , XING M D , SUN G C , et al. Minimum entropy via subspace for ISAR autofocus[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7 (1): 205- 209.
3	ZHOU L M, ZHANG X L, WANG Y Y, et al. Precise autofocus for SAR imaging based on joint multi-region optimization[C]//Proc.of the IEEE International Geoscience and Remote Sensing Symposium, 2019: 9137-9140.
4	WENDY G, WIllIAM P, RYAN M, et al. Performance evaluation of SAR/GMTI algorithms[C]//Proc.of the SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXⅢ, 2016, 9843: 5-18.
5	CAO P , XING M , SUN G , et al. Minimum entropy via subspace for ISAR autofocus[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7 (1): 205- 214.
6	CHEN Y C , LI G , ZHANG Q . Iterative minimum entropy algorithm for refocusing of moving targets in SAR images[J]. IET Radar, Sonar & Navigation, 2019, 13 (8): 1279- 1286.
7	ZHANG S H , LI X . Fast entropy minimization based autofocusing technique for ISAR imaging[J]. IEEE Trans.on Signal Processing, 2015, 63 (13): 3425- 3434.
8	马彦恒, 侯建强. 机动合成孔径雷达成像研究现状与发展趋势[J]. 兵器装备工程学报, 2019, 40 (11): 111- 115.
	MA Y H , HOU J Q . Resrarch status and development trend of maneuvering synthetic aperture radar imaging[J]. Journal of Ordnance Equipment Engineering, 2019, 40 (11): 111- 115.
9	AHMAD A Z , LIM T S , KOO V C , et al. A high efficiency gyrostabilizer antenna platform for real-time UAV synthetic aperture radar (SAR) motion error compensation[J]. Applied Mechanics and Materials, 2019, 892, 16- 22.
10	LU W T , GUO L P , XU W Q , et al. Vehicle detection in synthetic aperture radar images with feature fusion-based sparse representation[J]. Journal of Applied Remote Sensing, 2018, 12 (2): 025020.
11	卫扬铠, 曾涛, 陈新亮, 等. 典型线面目标合成孔径雷达参数化成像[J]. 雷达学报, 2020, 9 (1): 143- 153.
	WEI Y K , ZENG T , CHEN X L , et al. Parametric SAR imaging for typical lines and surfaces[J]. Journal of Radars, 2020, 9 (1): 143- 153.
12	ZHANG J , LIAO G S , ZHU S Q , et al. Wavenumber-domain autofocus algorithm for helicopter-borne rotating synthetic ap erture radar[J]. IET Signal Processing, 2018, 12 (3): 294- 300.
13	龙腾. 新体制民用雷达系统理论与关键技术[J]. 光学与光电技术, 2019, 17 (6): 6- 10.
	LONG T . Novel civilian radar system theory and key technolo gies[J]. Optics & Optoelectronic Technology, 2 019, 17 (6): 6- 10.
14	孟智超, 卢景月, 谢朋飞, 等. 无人机载多普勒分集前视合成孔径雷达成像方法[J]. 电子与信息学报, 2019, 41 (12): 2852- 2858.
	MENG Z C , LU J Y , XIE P F , et al. Imaging technology of doppler diversity forward-looking SAR imaging for unmanned aerial vehicle[J]. Journal of Electronics & Information Technology, 2019, 41 (12): 2852- 2858.
15	朱岱寅, 张营, 俞翔, 等. 微型合成孔径雷达成像信号处理技术[J]. 雷达学报, 2019, 8 (6): 793- 803.
	ZHU D Y , ZHANG Y , YU X , et al. Imaging signal processing technology for miniature synthetic aperture radar[J]. Journal of Radars, 2019, 8 (6): 793- 803.
16	毕辉, 张冰尘, 洪文, 等. 基于复图像的稀疏SAR成像方法在高分三号数据上的验证[J]. 雷达学报, 2020, 9 (1): 123- 130.
	BI H , ZHANG B C , HONG W , et al. Verification of complex image based sparse SAR imaging method on GaoFen-3 dataset[J]. Journal of Radars, 2020, 9 (1): 123- 130.
17	李宁, 牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法[J]. 雷达学报, 2020, 9 (1): 174- 184.
	LI N , NIU S L . High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9 (1): 174- 184.
18	WAN Y , ZHANG X Y , DAI Y S , et al. An azimuth cut-off wavelength compensation method based on multiview synthetic aperture radar synchronization data[J]. Remote Sensing Letters, 2020, 11 (3): 245- 254.
19	WANG L T , ZHANG M , WANG J K . Synthetic aperture radar image simulation of the internal waves excited by a submerged object in a stratified ocean[J]. Waves in Random and Complex Media, 2020, 30 (1): 177- 191.
20	LI D Y , WU J , WAN L , et al. Squint-looking differential synthetic aperture ladar[J]. Applied Optics, 2020, 59 (1): 129- 134.
21	HENNEN J A , DIERKING M P . Cross-correlation registration algorithm enhancements for multipixel in verse synthetic aperture LiDAR[J]. Appliedoptics, 2020, 59 (1): 217- 225.
22	BAO Q , YUAN S F , GUO F Y . A new synthesis aperture-MUSIC algorithm for damage diagnosis on complex aircraft structures[J]. Mechanical Systems and Signal Processing, 2020, 136, 106491.
23	TANG B, WANG M, ZHOU J. Experimental study of electrical compensation based on active phased ar ray antenna[C]//Proc.of the 7th Asia International Symposium on Mechatro-nics. Lecture Notes in Electrical Engineering, 2020, 588: 385-398.
24	王成浩, 廖桂生, 许京伟. FDA-SAR高分辨宽测绘带成像距离解模糊方法[J]. 电子学报, 2017, 45 (9): 2085- 2091.
	WANG C H , LIAO G S , XU J W , et al. Range ambiguity resolving method for high-resolution and wide-swath imaging with FDA-SAR[J]. Acta Electronica sinica, 2017, 45 (9): 2085- 2091.
25	VERESHCHAGIN A V, ZATUCHNY D A, SINITSYN V A, et al. The methods and algorithms of processing of signals improving observability and accuracy of the assessment of parameters of meteorological objects of airborne radar stations[C]//Proc.of the Signal Processing of Airborne Radar Stations, 2020: 97-186.

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基于MN-MEA算法的无人机载SAR相位误差补偿处理

Phase error compensation processing of UAV-borne SAR based on MN-MEA algorithm

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