Drift-Compensated UUV Velocity and Trajectory Estimation Using Hybrid Multisensor Fusion

Abstract

Inertial velocity estimation in underwater environments is fundamentally limited by unbounded drift arising from bias integration and the lack of external references. This article presents a hybrid flexible velocity sensor-based Kalman framework (HyFLEX-Vel-KF) that combines physics-based hydrodynamic sensing with stochastic state estimation. A hybrid flexible sensing architecture, denoted as the HyFLEX-Vel sensor, is developed to reconstruct flow-induced forces using a tridirectional flex rosette and a pressure-sensing element. The resulting force measurements are mapped to velocity through a calibrated quadratic drag model and incorporated as pseudomeasurements within a Kalman filtering framework. A complete stochastic state-space formulation is derived, including process and measurement noise characterization, covariance propagation, and observability analysis. The proposed approach enforces physically meaningful constraints on velocity evolution, thereby bounding drift accumulation inherent in inertial-only systems. Experimental validation demonstrates that the proposed HyFLEX-Vel-KF framework reduces the velocity root-mean-square error (RMSE) from $1.8072~\mathrm {m/s}$ [inertial measurement unit (IMU)-only] to $0.1138~\mathrm {m/s}$ , corresponding to an improvement of approximately 93.7 %, while reducing the drift rate by 1.0967 to $0.0402~\mathrm {m/s/min}$ magnitude which is 96.3 %. Finally, the total path error was reduced from 182.872 to 8.361 m, a 95.4 % improvement. Covariance analysis confirms bounded error propagation, and the normalized estimation error squared (NEES) evaluation remains within theoretical confidence bounds, indicating statistical consistency. Monte Carlo validation further confirms robustness under parameter variations and measurement noise. The proposed framework bridges physical sensing and optimal estimation, offering a practical and scalable solution for underwater navigation in GPS-denied environments.