Kalman Filter-based Sensor Fusion for Enhanced Position Measurement of Autonomous Underwater Vehicles - Sai Ganesh Palli; Manas Kumar Yogi (Journal of Electronics Design and Technology, 2025)
Accurate position measurement remains a critical challenge for autonomous underwater vehicles (AUVs) operating in complex marine environments where GPS signals are unavailable and environmental conditions introduce significant measurement uncertainties. This research presents a comprehensive investigation of Kalman filter-based sensor fusion techniques designed to enhance position estimation accuracy for AUVs through intelligent integration of multiple sensor modalities. The study develops an extended Kalman filter (EKF) framework that synergistically combines data from Doppler velocity logs (DVL), inertial measurement units (IMU), pressure sensors, and acoustic positioning systems to achieve robust and precise localization. Through rigorous mathematical modeling, we derive the state-space representation of AUV dynamics and measurement models, incorporating environmental disturbances and sensor noise characteristics. The proposed fusion architecture implements adaptive noise covariance estimation and outlier rejection mechanisms to maintain filter stability under adverse conditions. Simulation studies conducted across various underwater scenarios demonstrate that the integrated sensor fusion approach achieves position estimation errors below 0.5% of distance travelled, representing a 60–75% improvement over single-sensor dead reckoning methods. The research contributes novel insights into optimal sensor weighting strategies, adaptive filtering techniques for time-varying underwater environments, and practical implementation considerations for real-time AUV navigation systems. Results indicate that the Kalman filter framework successfully mitigates individual sensor limitations while exploiting their complementary characteristics, enabling autonomous underwater missions with extended duration and enhanced operational reliability.