A Quadratic Interpolation-Based Variational Bayesian Algorithm for Measurement Information Lost in Underwater Navigation

Abstract

The main challenges of sequential estimations of underwater navigation applications are the internal/external measurement noise and the missing measurement situations. A quadratic interpolation-based variational Bayesian filter (QIVBF) is proposed to solve the underwater navigation problem of measurement information missing or insufficiency. The quadratic interpolation is used to improve the observed vector for the precision and stability of sequential estimations when the environment is changed or the measurement information is lost. The state vector, the predicted error covariance matrix, and the measurement noise matrix are derived based on the variational Bayesian method. Simulation results demonstrate the superiority of the proposed QIVBF compared with the traditional algorithm under the condition of measurement information lost by autonomous underwater vehicles.

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

“…It consistently keeps the positioning error within 100m in the offline phase. Yang et al [96] used the quadratic interpolation (QI) method and the VB method to derive the predicted error covariance matrix and the measurement noise matrix. The Kalman filter was improved by nonlinearization to estimate the state vector and the observation vector more accurately.…”