Development of error reduction model using Bayesian filter for AUV navigating under moving ice

Abstract

Doppler Velocity Logs (DVL) can provide a simple under water navigation aid for Autonomous Underwater Vehicles (AUV) by measuring relative velocities with respect to the speed over ground. A valid reference velocity is difficult to calculate when this approach is applied under a moving frame of reference such as drifting ice. The primary challenge of under-ice localization is to accurately estimate the AUV location and its trajectory in the global coordinate system when DVL measurements are being made relative to a constantly drifting ice surface. In this paper, the author introduces and compares two types of error sources, scale factor error of DVL and navigation error due to ice drift. An error reduction model using a Bayesian filter algorithm is developed for improved estimations, in conjunction with a novel correction method for accurate AUV navigation under ice. The concept of shift factor is introduced in this paper as the key to solve both error sources. Having the knowledge of the true beacon location, shift factors in vector quantity are extracted based on the collected relative velocity profiles by DVL. The shift factors are directly applied to update the final AUV location. The result presents approximately 70.8% of maximum error reduction. The impact of survey pattern, bearing angle to the beacon, pinging frequency on the accuracy of the vehicle localisation are discussed.

Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle

“…The inverse transformation matrix R(θ) −1 , as shown in Equations (1)-(3), was used to transform the AUV's motion from the global reference frame to the local reference. The coordinates are interchangeable [11]. The global coordinates and headings were used for plume mapping, which is a function of the AUV's current pose, while local coordinates and headings were used in the decision-making environment.…”

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

“…The coordinates in both frames of reference are interchangeable [20]. The AUV pose (x, y, θ) in the GFR was used during plume mapping, while those in the LFR were used during the decision-making process.…”

Voronoi diagram with fuzzy number and sensor data in an indoor navigation for emergency situation

“…The latter focuses on the navigation models which study the optimal waypoints to the nearest or the fastest ways to the safe exits. This work normally integrates intelligent systems such as Bayesian filter [7][8][9] or fuzzy to obtain the best solution for specific conditions.…”