PVMD-Net: A Deep Network for Synchronization Error Compensation in Bistatic Imaging Radar

Due to inherent electromechanical constraints, a monostatic radar system is fundamentally incapable of achieving forward-looking imaging capabilities, and its imaging geometry is intrinsically limited by the platform’s physical configuration. The bistatic imaging radar (BIR) system, with its spatially separated transmitter and receiver, provides greater flexibility in imaging configurations and enables forward-looking imaging. However, this platform separation also introduces inherent time-frequency synchronization errors, significantly degrading imaging performance. While direct waves can currently be used to compensate for synchronization errors, the inaccuracy of the platform navigation system and multipath effects introduced by structures around the transmit/receive antennas prevent direct waves from precisely compensating for synchronization errors, making high-resolution imaging challenging. To address this issue, a BIR synchronization error compensation network based on variational mode decomposition (VMD) is proposed to achieve synchronization error compensation. First, echoes are preprocessed to extract the phase of multiple strong scatter points. Second, a VMD network tailored for phase decomposition is constructed to separate high-frequency noise and errors. Then, considering the spatial invariant of synchronization errors, joint polynomial estimation is used to estimate and compensate for synchronization errors. Finally, the backward projection algorithm is used to image the error-compensated echoes, and the autofocus algorithm is used to achieve high-resolution BIR imaging. The efficacy of the proposed algorithm is validated through multiple sets of point-target simulations incorporating synchronization errors derived from real-world BIR systems. Furthermore, bistatic UAV-borne SAR experiments were conducted in Tianjin, China, demonstrating that the proposed algorithm achieves superior high-resolution imaging performance compared to conventional methods.