Abstract
Terahertz Synthetic Aperture Radar (THz-SAR) is highly sensitive to platform vibrations and trajectory deviations, which introduce severe phase errors and limited resolution. Typically, platform vibrations and trajectory deviations are investigated individually, and vibrations are modeled as a stationary sine term. In this work, a hybrid motion compensation (MOCO) scheme is proposed to address both platform vibrations and trajectory deviations simultaneously, achieving improved imaging quality. The scheme initiates with a parameter self-adaptive quadratic Kalman filter designed to resolve severe phase wrapping. Then, platform vibration is modeled as a non-stationary multi-sine term, whose components are accurately extracted using an improved signal decomposition algorithm enhanced by a dynamic noise adjustment mechanism. Subsequently, the trajectory deviation is parameterized following subaperture division, estimated using a hybrid optimizer that combines particle swarm optimization and gradient descent. Additionally, a composite modulated waveform application ensures low sidelobes and a low probability of intercept (LPI). Extensive simulations on point targets and complex scenes under various signal-to-noise-ratio (SNR) conditions are applied for SAR image reconstruction, demonstrating robust suppression of motion errors. Under identical simulated error conditions, the proposed method achieves an azimuth resolution of 4.28 cm, which demonstrates superior performance compared to the reported MOCO techniques.