\r刘丽萍，操刘生，陈梦\r
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AuthorsHTML:\r刘丽萍，操刘生，陈梦\r
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AuthorsListE:\rLiu Liping，Cao Liusheng，Chen Meng\r
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AuthorsHTMLE:\rLiu Liping，Cao Liusheng，Chen Meng\r
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Unit:\r天津大学电气自动化与信息工程学院，天津 300072\r
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Unit_EngLish:\rSchool of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China

Abstract_Chinese:\r水下无线传感器网络(UWSNs)拓扑变化频繁，通信能力有限，给水下环境监测网络中的节点定位技术带来很大挑战．近海环境监测中，考虑到节点随着洋流移动并呈现出半周期性，利用节点运动模型，设计了基于 AR 模型和卡尔曼滤波的 UWSNs 节点分层预测定位方法(HPLM-AK)．建立了锚节点速度的 AR 预测模型，综合考虑网络能量消耗和定位精度的需求，进一步建立了锚节点速度的状态方程和观测方程，利用卡尔曼滤波算法实现对锚节点速度的最优估计，进而提高了定位精度且降低了网络通信能耗；普通节点利用与锚节点运动的空间相关性，根据参考节点的速度和位置信息估算自身的速度并结合上一时刻的位置信息完成定位．考虑到提高节点的定位覆盖度，设计了节点的定位置信度，并通过将置信度较高的普通节点升级为参考节点的方式来弥补锚节点稀疏的不足．同时，设计了参考节点列表更新机制，通过更新参考节点的信息，普通节点选取置信度较高的参考节点来参与自身的定位，提高了预测定位精度．本文以E117.25°～E132.20°、N24.00°～N43.45°洋流数据为实验背景对算法进行了仿真，并且与可扩展的移动预测定位(SLMP)方法进行了分析比较，仿真结果表明，HPLM-AK 方法提高了定位覆盖度和定位精度，且降低了网络的通信能耗．\r
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Abstract_English:\rUnderwater wireless sensor networks(UWSNs) have frequent topology changes and limited communication capabilities，which pose great challenges to localization in underwater environment monitoring network. In seashore monitoring networks，considering that node mobility pattern shows certain semi-periodic property，a hierarchical prediction localization method based on AR model and Kalman algorithm(HPLM-AK)is proposed for UWSNs. Considering the requirements of network energy consumption and localization accuracy，the state equation and the observation equation of anchor node velocity based on AR prediction model are established. Kalman algorithm is used to estimate the optimal anchor node velocity information to improve the localization accuracy and reduce communication cost. According to the spatial correlation between ordinary nodes and anchor nodes，the speed and the position information of anchor nodes are used to locate ordinary nodes. To improve the localization coverage，the localization confidence value is designed to upgrade ordinary nodes to reference nodes to compensate for the sparseness of anchor node. Moreover，an update mechanism of the reference node list，which can update the information of reference nodes in time is designed to improve the accuracy of ordinary nodes localization. The simulation results based on ocean data in the region from E117.25° to E132.20° and from N24.00° to N43.45° show that the HPLM-AK method has higher localization coverage and lower localization error and communication cost than the scalable localization with mobility pattern(SLMP)method.\r
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Keyword_Chinese:水下传感器网络；AR模型；卡尔曼滤波；参考节点列表；分层预测定位\r

Keywords_English:underwater wireless sensor networks；AR model；Kalman filter；reference list；hierarchical prediction localization\r


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