State Estimation of the Underwater Moving Target Based on Multi-Sensor Information Fusion

DOI

10.2991/wiet-13.2013.45How to use a DOI?

Keywords

State estimation; Underwater moving target; Multi-sensor information fusion

Abstract

The Kalman filter has been widely used in state estimation of moving targets. Furthermore, the well-known conventional Kalman filter requires an accurate system model and exact prior information. However, the nonlinearity, dynamic and randomicity in the underwater environment result in uncertainty and incontinuity of the observation information and unknown bias of the system model, which may seriously degrade the performance of the Kalman filter or even cause the filter to diverge. Therefore, a novel filtering algorithm based on multi-sensor information fusion estimation theory and dynamic bayesian network inference is presented, which is based on the idea of fusing firstly and then filtering. Firstly, the multi-sensor system fuses the sub-systems measurement information to obtain more accurate initial measurement information and covariance information, and then smooth missing data and fuzzy data by fusing the the obtained system state predictive information and all the measurement information of the sub-systems to obtain the accurate state estimation of underwater moving target. Finally, the simulation results show that the proposed method can efficiently estimate underwater target state without prior noise information, and can evidently improve the state estimation precision of underwater moving target by adjusting the weight factor despite noise-related.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zheng Tang
AU  - Chao Sun
AU  - Zong-wei Liu
AU  - Di Meng
PY  - 2013/12
DA  - 2013/12
TI  - State Estimation of the Underwater Moving Target Based on Multi-Sensor Information Fusion
BT  - Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
PB  - Atlantis Press
SP  - 189
EP  - 192
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.45
DO  - 10.2991/wiet-13.2013.45
ID  - Tang2013/12
ER  -