A One-way Prediction Based Joint Self-localization and Synchronization Algorithm for Mobile Underwater Acoustic Asynchronous Networks
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摘要: 节点位置的实时变化以及水下长传输时延等特性影响了移动水声异步网络(MUAANs)的时间同步与自定位精度。针对上述问题，该文研究并提出一种自定位和时间同步联合的单向动态预测算法。该算法通过建立时间同步与自定位联合的状态与观测模型，同时预测不同时刻下移动节点的位置信息与时钟差异，实现网络的联合位置跟踪与动态时间同步。与此同时，由于该算法仅采用节点间的单向信息传输即可实现自定位与时间同步，有效解决了由水下长传输时延导致的移动节点间双向信息交互时延差异的问题，提高了网络的自定位与时间同步精度和效率。仿真结果表明，该文所提算法可以高精度地联合预测移动水声异步网络的自定位与时间同步结果，且有效增强了网络的时间同步与自定位时间效率。Abstract: In order to solve the problems of low time synchronization and positioning accuracy for Mobile Underwater Acoustic Asynchronous Networks(MUAANs)caused by nodes movement and long communication time delay, a one-way prediction based joint self-localization and synchronization algorithm is proposed. In this algorithm, each mobile node’s position and time synchronization can be predicted simultaneously by constructing a joint state model and observation model. Meanwhile, the one-way communication is used between each node in the network which can solve the problem of changed communication time delay when nodes are moving at different time and improve the accuracy and time efficiency of self-localization and time synchronization. Simulation results show that the proposed algorithm can predict the mobile node’s position and time synchronization jointly and increase the accuracy and time efficiency of self-localization and time synchronization simultaneously which is more suitable for underwater environment.
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