Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Brief paper: Optimal position and velocity navigation filters for autonomous vehicles
Automatica (Journal of IFAC)
Robust Kalman filter based on a generalized maximum-likelihood-type estimator
IEEE Transactions on Signal Processing
Vehicle velocity estimation using nonlinear observers
Automatica (Journal of IFAC)
Output-feedback control of an underwater vehicle prototype by higher-order sliding modes
Automatica (Journal of IFAC)
Filtering and control performance bounds with implications on asymptotic separation
Automatica (Journal of IFAC)
Bounds on the accuracy attainable in the estimation of continuous random processes
IEEE Transactions on Information Theory
| Hi-index | 22.14 |
This paper presents a novel approach to the design of globally asymptotically stable (GAS) position and velocity filters for Autonomous Underwater Vehicles (AUVs) based directly on the sensor readings of an Ultra-short Baseline (USBL) acoustic array system and a Doppler Velocity Log (DVL). The proposed methodology is based on an equivalent linear time-varying (LTV) system that fully captures the dynamics of the nonlinear system, allowing for the use of powerful linear system analysis and filtering design tools that yield GAS filter error dynamics. Numerical results using Monte Carlo simulations and comparison to the Bayesian Cramer Rao Bound (BCRB) reveal that the performance of the proposed filter is tight to this theoretical estimation error lower bound. In comparison with other approaches, the present solution achieves the same level of performance of the Extended Kalman Filter (EKF), which does not offer GAS guarantees, and outperforms other classical filtering approaches designed in inertial coordinates instead of the body-fixed coordinate frame.