基于CNN-LSTM声速预测的水下移动节点定位算法  

Underwater mobile node location algorithm based on CNN-LSTM sound velocity prediction

作　　者：彭铎[1] 查海音 曹坚 张彦博 张明虎 Peng Duo;Zha Haiyin;Cao Jian;Zhang Yanbo;Zhang Minghu(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050

出　　处：《电子测量与仪器学报》2024年第11期146-157,共12页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金面上项目(42271492);甘肃省杰出青年基金(24JRRA165)项目资助。

摘　　要：本文旨在解决水下无线传感器网络中因水下环境复杂多变导致的长时延问题,该问题显著影响移动传感器节点间的信息传播效率,进而增大了节点定位误差。为此,本研究创新性地提出了一种基于CNN-LSTM声速预测的水下移动节点定位算法。首先,通过K-折交叉验证法对声速数据集进行科学划分,随后构建并训练了一个融合卷积神经网络(CNN)特征提取能力与长短期记忆网络(LSTM)序列建模能力的CNN-LSTM混合模型。此模型有效捕捉了声速数据中的空间与时间特征,显著提升了声速预测的准确度。在定位过程中,采用该模型预测的声速值进行到达时间差(TDOA)测距,并据此对测距结果进行精细修正。进而,针对不同节点密度条件下的未知节点,算法能够自适应地选择最适宜的测距定位方法,依据参考节点数量实现精准定位。实验结果显示,与现有的SLMP、DMP、NDSMP及BLSM定位算法相比,本文提出的MCLS定位算法在相同信标节点条件下,定位误差均值分别降低了46.96%、39.93%、27.64%和15.24%,显著提升了水下移动节点的定位精度与稳定性。This study addresses the long delay issue in underwater wireless sensor networks(UWSNs)caused by the spatio-temporal complexity and dynamics of the underwater environment,which significantly impacts the information propagation between mobile sensor nodes and consequently leads to large node localization errors.To this end,a novel underwater mobile node localization algorithm based on CNN-LSTM sound speed prediction is proposed.Initially,the sound speed dataset is partitioned using the K-fold cross-validation method.Subsequently,a hybrid CNN-LSTM model is constructed and trained,leveraging the feature extraction capability of CNN and the sequence modeling strength of LSTM.This model efficiently captures both spatial and temporal information from the sound speed dataset,thereby enhancing the prediction accuracy.During the localization process,the predicted sound speed values from the CNNLSTM model are employed for time difference of arrival(TDOA)ranging,and the ranging values are refined accordingly.Finally,the refined ranging values are utilized to adaptively select the optimal ranging and localization method for unknown nodes under varying node densities,based on the number of reference nodes,thereby achieving precise localization of underwater mobile nodes.Experimental results demonstrate that,compared to existing localization algorithms such as SLMP,DMP,NDSMP,and BLSM,the proposed MCLS localization algorithm reduces the mean localization error by 46.96%,39.93%,27.64%,and 15.24%,respectively,under the same beacon node conditions,significantly improving the localization accuracy and stability of underwater mobile nodes.

关 键 词：水下传感器网络 声速预测 CNN-LSTM模型 距离修正 移动节点定位 

分 类 号：TN92[电子电信—通信与信息系统]