GRFT-Based Moving Ship Target Detection and Imaging in Geosynchronous SAR

Abstract

Geosynchronous synthetic aperture radar (GEO SAR) has great potentials in ship surveillance due to its high time resolution and wide swath coverage. However, the remote slant range will result in a very low signal-to-noise ratio (SNR) of echoes that need to be enhanced by long-time coherent integration. The generalized Radon-Fourier transform (GRFT) can realize the coherent integration of moving target under long integration time by jointly parameter searching along range and velocity directions. Unfortunately, in GEO SAR, the very large slant range and long synthetic aperture will cause the curved synthetic aperture trajectory and non-negligible signal round-trip delay, leading to the failure of the traditional slant range and GRFT signal model for moving targets. This paper proposes an improved GRFT-based approach to realize the detection and imaging of moving ship targets in GEO SAR. Firstly, the accurate slant range for moving ship targets is constructed and the GRFT signal is redefined considering the curved trajectory and signal round-trip delay in GEO SAR. Then, GRFT responses to different motion parameters are analyzed. The procedures of moving ship targets detection and imaging in GEO SAR are presented through the detection with coarse-searched motion parameters in GRFT and the following imaging with fine-searched motion parameters based on minimum entropy. Finally, computer simulations verify the proposed GRFT-based method. be observed within the range of every 1.5-2.5 h per day [14]. Thus, GEO SAR has great advantages in ship surveillance over vast ocean area.Currently, there are growing interests for GEO SAR, which are mainly concentrated on system design and mission planning [15][16][17][18][19][20][21], imaging algorithms [22][23][24][25], ionospheric effects and compensation [26][27][28], and deformation retrieval [29][30][31]. However, there are rarely related researches on detection and imaging of moving ships.Obviously, due to high time resolution and wide swath coverage, GEO SAR has great potential in monitoring moving ships. However, the remote slant range of GEO SAR will result in a very low signal-to-noise ratio (SNR) of echoes. The defect of low SNR can be compensated for the static target through long time integration [32]. Unfortunately, the parameters of moving targets are unknown, leading to the failure of coherent integration and the resultantly SNR loss. Therefore, traditional detection and imaging methods for moving targets used in LEO SAR and airborne SAR are not suitable for GEO SAR.Moving targets detection and imaging can be divided into single-channel and multi-channel SAR schemes according to SAR system configuration. Because of the limitation of low SNR, multichannel configuration cannot be realized currently, as it is mainly designed for clutter suppression where ships have higher radar cross section (RCS) against the surrounding sea background (in general, ships has higher backscattering energy as the metallic materials and corner reflection structures [33,34]). Therefore, mo...

Geosynchronous Spaceborne-Airborne Bistatic Moving Target Indication System: Performance Analysis and Configuration Design

“…SCNR loss is one of the important performance indicators and it varies with θ R and φ. SCNR loss of the first four configurations of GEO SA-BSAR MTI in Table 4 will be simulated. The simulation results are in Figure 3, where the solid line is the results by the patch-based approach (calculating SCNR loss by exploiting the echoes of moving target and clutter patches [38]) while the '*' is calculated by using Equation (22). It can be seen that the analytical expression and the actual curve are fitting very well, which can be used to calculate the MDV and MUV.…”

Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR

“…The simulation was also conducted to verify the range model's accuracy based on the curved trajectory and near-field effects in Section 2. The range model is shown in Equation (20). It was assumed that the radial velocity and azimuth velocity were all 30 m/s, and the error is shown in Figure 8.…”

SAR imaging of multiple maritime moving targets based on sparsity Bayesian learning

“…The focused image was generated by the orthogonal matching pursuit algorithm, where the sparsity was required to be a priori known. Moreover, a moving ship target detection and imaging method based on the generalised radon‐Fourier transform was proposed in [15]. Lian and Jiang [16] studied the time–frequency characteristics of moving ship targets using the Wigner–Ville distribution (WVD), the pseudo WVD, and the chirplet decomposition in the geosynchronous spaceborne/airborne bistatic SAR system.…”