TY - JOUR
T1 - Passive Multistatic Radar Imaging of Vessel Target Using GNSS Satellites of Opportunity
AU - Huang, Chuan
AU - Li, Zhongyu
AU - An, Hongyang
AU - Sun, Zhichao
AU - Wu, Junjie
AU - Yang, Jianyu
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Global navigation satellite system (GNSS)-based passive radar
KW - maritime surveillance
KW - passive radar imaging
KW - satellite signals of opportunity
KW - space-surface multistatic radar
UR - https://www.scopus.com/pages/publications/85135744660
U2 - 10.1109/TGRS.2022.3195993
DO - 10.1109/TGRS.2022.3195993
M3 - 文章
AN - SCOPUS:85135744660
SN - 0196-2892
VL - 60
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 5116416
ER -