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.
Highlights
The accurate measurements are difficult for the underwater integrated navigation system to describe because the performance of sensors varies with the change in the environment and measurement information probability lost [25, 26]. e joint probability density function (PDF) is used to model the information of the autonomous underwater vehicles (AUVs). en, the information of the joint PDF is determined by using the variational Bayesian (VB) method [27] in recursive process, which is the optimal
Aiming to mitigate the underwater navigation problem of measurement information missing or insufficiency, this paper proposes a quadratic interpolation-based variational Bayesian filter (QIVBF) algorithm. e QIVBF makes better use of the quadratic interpolation (QI) method and the VB method, so that the predicted error covariance matrix and the measurement noise matrix are derived to estimate the state vector more accurately. e quadratic interpolation improves the observed vector for the precision and stability of sequential estimations when the environment is changed or the measurement information is lost
Simulations and Results e proposed QIVBF is compared with the traditional Kalman filter (KF) in a simulation with measurement missing scenarios, and the root mean squared error (RMSE) is defined to compare the performances of these algorithms.
Summary
Ocean has already become the strategic goal of many countries because of its underdeveloped resources, marine environments, and high-tech fields [1, 2]. e autonomous underwater vehicles (AUVs) have become one of the important tools for underwater detection, environment survey, and underwater reconnaissance in the ocean [3, 4]. Aiming to mitigate the underwater navigation problem of measurement information missing or insufficiency, this paper proposes a quadratic interpolation-based variational Bayesian filter (QIVBF) algorithm. E QIVBF makes better use of the quadratic interpolation (QI) method and the VB method, so that the predicted error covariance matrix and the measurement noise matrix are derived to estimate the state vector more accurately. An underwater navigation function with measurement information lost is described as follows [36]:. Υk means the measurement noise, which follows the Gaussian distribution with zero mean vector and measurement error covariance Rk. e fk− 1(·) is the known process function at the time k − 1. ∇bz εbx εby εbz Mx My Mz ]Twith δL, δλ, and δh denoting the latitude, longitude, and height position error in ENU axes, respectively; δVE, δVN, and δVU are the east, north, and upward velocity errors in ENU axes, respectively; φE, φN, and φU represent the heading, pitch, and roll errors in ENU axes, respectively; ∇bx, ∇by, and ∇bz set as the biases of the accelerometer projections onto the ENU axes; εbx, εby, and εbz for the gyroscope drift projections onto the ENU axes, respectively; Mx, My, and Mz are the measured magnetic field vector in the ENU axes, respectively
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