Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

In the field of maritime surveillance, the global navigation satellite system (GNSS)-based passive radar has proven its potential for moving target detection (MTD), localization, and velocity estimation. The next stage is to investigate the possibility of obtaining the radar image of the moving ship for target recognition. However, the limited signal power budget of GNSS prevents the conventional inverse synthetic aperture radar technique that is based on target rotational motion and short observation time for GNSS-based passive radar imaging moving target. In this article, a two-stage imaging processing method relying on the target translational motion over a long observation time is proposed. The first stage confirms the presence of the target by a long-time MTD processing technique. In the second stage, based on the analysis of the Doppler history of the target signal in the slow-time domain, short-time Fourier transform and modified random sample consensus are combined to robustly estimate target velocity with reduced computation complexity. To obtain the focused bistatic image, azimuth compression is conducted by using the estimated target velocity. Finally, an image fusion operation is implemented to combine the bistatic images achievable from multiple satellites so that a multistatic image with high quality can be created. The effectiveness of the proposed method is confirmed by the real experimental results of three cargo ships illuminated by several satellites.

Automated summary

In this article, a two-stage imaging processing method is proposed to obtain the synthetic aperture radar (SAR) image of a moving ship by utilizing the target's translational motion over a long observation time. The first stage confirms the presence of the target by a long-time moving target detection (MTD) processing technique. In the second stage, the target velocity is accurately estimated by analyzing the Doppler history of the target signal in the slow-time domain using a short-time Fourier transform and modified random sample consensus.