| 彭 铎,曹 坚,黎亚亚.基于动态贝叶斯 LS-SVM 的水下节点移动预测定位算法[J].电子测量与仪器学报,2023,37(10):134-144 |
| 基于动态贝叶斯 LS-SVM 的水下节点移动预测定位算法 |
| Underwater node movement prediction and positioning algorithm based on dynamic bayes LS-SVM |
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| DOI： |
| 中文关键词: 水下无线传感器网络 贝叶斯证据框架 自适应增减算法 移动预测 定位算法 |
| 英文关键词:UWSN Bayesian evidence framework adaptive increment and subtraction algorithm mobile prediction location algorithm |
| 基金项目:国家自然科学基金(61663024,62061024)、甘肃省高校创新基金(2020A-021)项目资助 |
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| 摘要点击次数: 585 |
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| 中文摘要: |
| 针对水下无线传感器网络环境的复杂性和节点的动态性所导致的节点定位精度低的问题,提出了一种基于动态贝叶斯
LS-SVM 的水下无线传感器网络节点移动预测定位算法;该算法以信标节点到通信半径内所有信标节点的距离和跳数矩阵作
为训练集;利用贝叶斯证据框架构建贝叶斯 LS-SVM 模型,将未知节点与信标节点之间的跳数向量作为测试集;将测试集代入
到训练好的贝叶斯 LS-SVM 模型中来确定节点之间的距离,进而建立节点与信标节点距离矩阵的方程并利用最大似然估计法
对未知节点坐标进行估算;最后,通过循环迭代的方式对所有未知节点进行定位的同时使用自适应增减算法动态调整模型参数
和预测模型,以适应数据的动态变化;实验结果表明,该算法相同的节点密度下相较于 SLMP 算法、RTLC 算法、NDSMP 算法以
及 MPL 算法的平均定位误差分别降低了 24. 77%、22. 25%、3. 1%、6. 5%,有效地实现了水下未知节点的动态定位。 |
| 英文摘要: |
| In order to solve the problem of low node positioning accuracy caused by complexity of underwater wireless sensor network
environment and node dynamics, a node movement prediction and positioning algorithm based on dynamic Bayes LS-SVM was proposed
in this paper. In this algorithm, the distance and hop matrix from the beacon node to all beacon nodes within the communication radius
are used as the training set, and the normalization process is carried out. The Bayesian LS-SVM model was constructed using Bayesian
evidence framework, and the hop number vector between the unknown node and the beacon node was taken as the test set to determine
the distance between the node and the beacon node. Then the equation of the distance matrix between the node and the beacon node was
established and the maximum likelihood estimation method was used to estimate the coordinates of the unknown node. Finally, all
unknown nodes were located by iterative method, and the adaptive increment and subtraction algorithm was used to dynamically adjust
the model parameters and prediction model to adapt to the dynamic changes of data. The experimental results show that the average
positioning error of the algorithm is reduced by 24. 77%, 22. 25%, 3. 1%, and 6. 5% compared with the SLMP algorithm, RTLC
algorithm, NDSMP algorithm, and MPL algorithm under the same node density, effectively realizing underwater positioning. Dynamic
positioning of unknown nodes. |
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