期刊
OCEAN ENGINEERING
卷 266, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2022.112890
关键词
Adaptive Kalman filter; Target tracking; Position; Velocity; Radar; Unmanned surface vehicle
资金
- Nature Science Foundation of China [61976033, 51609033]
- Nature Science Foundation of Liaoning Province of China [20180520005]
- Fundamental Research Funds for the Central Universities [3132021106]
This paper proposes an improved filtering algorithm to address the issue of poor observation caused by strong vibration in unmanned surface vehicles during navigation, and achieves superior performance in target tracking.
The filtering methods are crucial for an unmanned surface vehicle (USV) to realize target tracking. Due to the poor observation caused by the strong vibration during the navigation of the USV, the target tracking accuracy of the traditional filtering method has been significantly degraded. A modified strong tracking-based expended Sage-Husa adaptive robust Kalman filter (MST-ESHARKF) algorithm is proposed to overcome this problem in this paper. In the proposed algorithm, a modified fading factor for the strong tracking Kalman filter is introduced to eliminate disturbance-induced filter divergence. In addition, the adaptive factor of robust Kalman filtering is designed to balance the predicted and observed states dedicated to improving the robustness of the algorithm. Finally, the biased and unbiased estimators for measurement and process noise covariances are merged, and the measurement noise covariance matrix's interval is constrained, resulting in a simultaneous evaluation of measurement and process noise covariance matrices with improved dependability of the proposed algorithm. The simulation and experiment results show that the proposed MST-ESHARKF outperforms the existing filters in target tracking.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据