Optimized Quadratic Gaussian Framework For Real-Time Terrain-Aided Navigation Of Auvs Using Synthetic Aperture Radar

Abstract

Autonomous Underwater Vehicles (AUVs) require precise navigation solutions in environments where conventional positioning systems, such as GPS, are unavailable. To address this challenge, we propose QGOG-SAR (Quadratic Gaussian Optimized Synthetic Aperture Radar Framework), a novel methodology for real-time terrain-aided navigation. The framework integrates a quadratic Gaussian optimization strategy with synthetic aperture radar (SAR) processing to enhance phase unwrapping accuracy and Digital Elevation Model (DEM) fusion. By incorporating adaptive Gaussian modeling, QGOG-SAR mitigates noise sensitivity, reduces phase ambiguities, and improves feature extraction from complex seabed terrains. Simulation experiments conducted in MATLAB demonstrate that the proposed framework significantly outperforms conventional SAR-based techniques in terms of navigation accuracy, robustness, and computational efficiency. The results confirm that QGOG-SAR provides a reliable and scalable solution for real-time underwater terrain navigation, offering strong potential for applications in exploration, defense, and environmental monitoring.