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This work proposes a hybrid motion compensation scheme that simultaneously addresses
non-stationary platform vibrations and trajectory deviations, overcoming the
limitations of conventional methods that treated them individually. The proposed method achieves high-precision azimuth resolution across a range of SNR
conditions, demonstrating superior performance compared to reported techniques.
It provides a robust and practical solution for high-resolution THz-SAR imaging, paving
the way for vibration suppression in scenarios where platform motion is complex and
non-stationary. The integrated approach of adaptive filtering, advanced signal decomposition, and
hybrid optimization establishes a new benchmark for motion-error suppression in
advanced radar systems. 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.
Details
Waveforms;
Image resolution;
Sidelobes;
Vibration control;
Optimization;
Unmanned aerial vehicles;
Image processing;
Adaptive filters;
Vibration;
Kalman filters;
Vibrations;
Image reconstruction;
Fourier transforms;
Trajectories;
Synthetic aperture radar;
Antennas;
Radar equipment;
Deviation;
Decomposition;
Motion compensation;
Errors;
Robustness (mathematics);
Azimuth;
Compensation;
Parameter estimation;
Signal to noise ratio
1 School of Integrated Circuits, Shandong University, Jinan 250100, China; [email protected] (C.W.); [email protected] (Y.S.); [email protected] (X.Z.)
2 School of Integrated Circuits, Shandong University, Jinan 250100, China; [email protected] (C.W.); [email protected] (Y.S.); [email protected] (X.Z.), Shandong Key Laboratory of Metamaterial and Electromagnetic Manipulation Technology, Jinan 250100, China