Passive Multistatic Radar Imaging of Vessel Target Using GNSS Satellites of Opportunity

被引:11
|
作者
Huang, Chuan [1 ]
Li, Zhongyu [1 ]
An, Hongyang [1 ]
Sun, Zhichao [1 ]
Wu, Junjie [1 ]
Yang, Jianyu [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
基金
中国国家自然科学基金;
关键词
Satellites; Global navigation satellite system; Radar imaging; Passive radar; Imaging; Doppler effect; Transmitters; Global navigation satellite system (GNSS)-based passive radar; maritime surveillance; passive radar imaging; satellite signals of opportunity; space-surface multistatic radar;
D O I
10.1109/TGRS.2022.3195993
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The global navigation satellite system (GNSS)-based passive radar shows potential for permanent maritime surveillance. In this article, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated with the range and Doppler (RD) domain after removing the 2-D migrations. The signal-to-noise ratio (SNR) can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel's velocity is set as a decision variable and the aim is to minimize the image entropy. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world's first BeiDou-based passive radar maritime experiment, and the effectiveness of the proposed method is verified against the experimental data.
引用
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页数:16
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