Cyber-Physical Cooperative Localization Algorithms for Underwater Vehicle With Asynchronous Time Clock
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摘要: 在异步时钟下研究了一种基于信息物理融合的水下潜器协同定位问题.首先，构建了由浮标、传感器和潜器组成的水下信息物理融合系统架构.然后，考虑水下异步时钟影响，设计了基于传感器与潜器交互通信的异步定位策略，给出了潜器协同定位问题.为求解上述协同定位问题，分别提出了基于扩展卡尔曼滤波（Extended Kalman filter，EKF）与无迹卡尔曼滤波（Unscented Kalman filter，UKF）的水下潜器协同定位算法.最后，对上述定位算法的有界性以及克拉美罗下界（Cramér-Rao lower bound，CRLB）进行了分析.仿真结果表明，上述算法可有效消除异步时钟对水下定位的影响.同时基于无迹卡尔曼滤波的定位算法可提高定位精度.Abstract: With consideration of asynchronous time clock, this paper studies a cyber-physical cooperative localization problem for underwater vehicle. We firstly construct an architecture of underwater cyber-physical system, including buoys, sensors and vehicle. Then, considering the influence of asynchronous time clock, we design an asynchronous localization strategy which is based on the cooperation communication between sensors and vehicle, and we formulize the cooperative localization problem for underwater vehicle. In order to solve this cooperative localization problem, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) based cooperative localization algorithms are proposed, respectively. Finally, we give the boundedness and Cramér-Rao lower bound (CRLB) for these algorithms. Simulation results show that the proposed algorithms can effectively eliminate the influence of asynchronous time clock on underwater localization. Meanwhile, the UKF-based localization algorithm can improve the localization accuracy.
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Key words:
- Localization /
- underwater vehicle /
- cyber physical systems /
- asynchronous /
- underwater sensor networks
1) 本文责任编委 孟凡利 -
图 3 同步测量与异步测量距离比较
Fig. 3 Distance comparison with synchronous and asynchronous measurements
图 4 同步测量与异步测量距离误差比较
Fig. 4 Distance error comparison with synchronous and asynchronous measurements
图 5 穷举法、扩展卡尔曼和无迹卡尔曼定位轨迹
Fig. 5 Trajectories with exhaustive, EKF-based and UKF-based methods
图 6 穷举法、扩展卡尔曼与无迹卡尔曼MSE比较
Fig. 6 Comparison of MSE for exhaustive, EKF and UKF based methods
图 7 扩展卡尔曼与无迹卡尔曼MSE与CRLB比较
Fig. 7 Comparison of MSE and CRLB for UKF and EKF-based methods
图 8 算法仿真定位用时比较
Fig. 8 Localized trajectories of underwater vehicle different interference intensities
图 9 不同干扰强度下潜器定位轨迹
Fig. 9 Positioning trajectories of submersible under different interference intensities
图 10 不同干扰强度下潜器定位误差
Fig. 10 Positioning errors of underwater vehicle under different interference intensities
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