State Estimation of the Underwater Moving Target Based on Multi-Sensor Information Fusion
Zheng Tang, Chao Sun, Zong-wei Liu, Di Meng
Available Online December 2013.
- https://doi.org/10.2991/wiet-13.2013.45How to use a DOI?
- State estimation; Underwater moving target; Multi-sensor information fusion
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 189 EP - 192 SN - 1951-6851 UR - https://doi.org/10.2991/wiet-13.2013.45 DO - https://doi.org/10.2991/wiet-13.2013.45 ID - Tang2013/12 ER -