High-Resolution BVLOS Radar Imaging Method for Noncooperative Complex Maneuvering Targets

被引:0
|
作者
Wang, Xin [1 ]
Yang, Jing [1 ,2 ]
Pu, Youlei [1 ,2 ]
Wen, Zhijin [1 ,2 ]
Wang, Li [1 ,2 ]
Luo, Yong [1 ,2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 611731, Peoples R China
[2] State Key Lab Electromagnet Space Cognit & Intelli, Beijing 100089, Peoples R China
基金
中国国家自然科学基金;
关键词
Imaging; Radar imaging; Sensors; Radar; Autonomous aerial vehicles; Heuristic algorithms; Mathematical models; Beyond visual line of sight (BVLOS) radar sensing imaging; inverse synthetic aperture radar (ISAR); multiparameter collaborative sensing imaging framework; noise robust; MOTION COMPENSATION; ISAR IMAGES; PARAMETER-ESTIMATION; FOURIER-TRANSFORM; ALGORITHM; AUTOFOCUS; SAR;
D O I
10.1109/JSEN.2024.3455101
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The rapid growth of commercial aviation and the increasing prevalence of small targets such as unmanned aerial vehicles (UAVs) have underscored the critical need for advanced beyond visual line of sight (BVLOS) radar situational awareness techniques, particularly for UAV detection. High-resolution imaging of BVLOS targets is paramount for accurate target sensing. Unlike traditional large-size targets, UAVs exhibit weak radar returns and complex, high-order time-varying dynamics, posing significant challenges for detection and tracking. Existing methods often focus on sensing imaging designed for large, less maneuverable targets, thereby limiting effectiveness in imaging UAVs with distinct high-order time-varying characteristics. To address these challenges, this article proposes a multiparameter collaborative sensing imaging framework driven by inverse synthetic aperture radar (ISAR) technology. Leveraging the principles of ISAR, the framework analyzes the relationship between far-field echo characteristics of targets and their complex 3-D high-order time-varying motion, establishing an innovative equation for high-order far-field echoes. Based on this analysis, we introduce a novel multiparameter dynamic imaging algorithm that exploits variations in target surface point scattering intensity to construct high-resolution sensing images of targets. This approach offers enhanced noise robustness compared to conventional cascaded-mode methods. Furthermore, extensive simulations and real-data experiments validate the superior performance of the proposed algorithm over existing methodologies.
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页码:32919 / 32935
页数:17
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