测距
构造(python库)
算法
趋同(经济学)
计算机科学
人工神经网络
职位(财务)
高斯分布
反向传播
遗传算法
人工智能
机器学习
电信
物理
财务
量子力学
经济
程序设计语言
经济增长
作者
Peiwen Zhang,Zhonghua Liang,Jiaye Hu,Xin He,Wei Li
标识
DOI:10.1109/icbaie56435.2022.9985915
摘要
Traditional localization algorithms based on received signal strength indication (RSSI) construct ranging model through the log-normal shadowing model, in which the parameters are usually chosen empirically and therefore sensitive to the influence of environments. In this paper, an indoor localization method based on sparrow search algorithm (SSA) and backward propagation (BP) neural network is proposed to construct the ranging model. In the proposed SSA-BP method, the collected RSSI values are firstly processed by Gaussian filtering and then input into the SSA-BP neural network to construct the ranging model, which can output distance values between the target nodes (TN) and anchor nodes. Finally, the TN's position can be estimated by using the maximum likelihood estimation (MLE) method. Simulation results show that compared with the existing BP algorithm and genetic algorithm (GA), the proposed SSA-BP algorithm has faster convergence speed and higher positioning accuracy.
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