基于深度神经网络的水下无线传感器网络距离定位:海报摘要

Yuhan Dong, Zheng Li, Rui Wang, Kai Zhang
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引用次数: 3

摘要

在水下无线传感器网络(USWNs)中,未知节点的定位在大多数应用中是必不可少的,但比地面无线传感器网络的定位更为复杂。本文提出了一种基于深度神经网络(DNN)的距离定位方案。数值结果表明,所提出的深度神经网络定位算法在定位精度和效率方面优于传统的最小二乘支持向量机(LS-SVM)和广义最小二乘(GLS)方案。此外,该算法需要较少的锚节点,在实际应用中是合理的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Range-based localization in underwater wireless sensor networks using deep neural network: poster abstract
In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines (LS-SVM) or generalized least squares (GLS) in terms of localization accuracy and efficiency. Moreover, the proposed algorithm requires a small number of anchor nodes, which is plausible for practical applications.
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