Abstract
An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.
Highlights
For target tracking in the ocean environment, two approaches are generally used
The black lines show the positioning error of the tracking method based on extended Kalman filter (TT-EKF) with the process noise covariance matrix (PNCM) is estimated by variational Bayesian (VB) algorithm and the odometric error is compensated by BP neural network
The estimated heading and forward speed of Autonomous Underwater Vehicle (AUV) and mobile recovery system (MRS) are shown in Figure 8 based on TT-EKF with the PNCM is estimated by VB algorithm and the prediction error is compensated by BP neural network
Summary
For target tracking in the ocean environment, two approaches are generally used. Linear. A variety of filters were proposed to address the state estimation problem with unknown noise covariance matrixes, such as robust KF [7,8], adaptive KF [9,10,11,12,13,14,15], and others [16]. In [13], the online state estimation with inaccurate PNCM is treated, in which the prediction error covariance matrix rather than PNCM is approximated by the VB method. It cannot give the estimated value of the unknown PNCM. We address the online state estimation problem in the presence of the unknown and non-Gaussian PNCM. We build a neural network compensator to make up for the prediction error caused by the process noise
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