RSSI ranging optimization algorithm based on BP neural network
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摘要: 基于接收信号强度指示(received signal strength indication, RSSI)测距的研究应用领域很广泛,一直是物联网研究的热点. 为降低传统基于反向传播(back propagation,BP)神经网络的RSSI测距误差,文中提出一种基于K-means聚类算法对样本数据进行预处理的BP神经网络测距方法,来解决由于RSSI值衰减程度不同引起的不同距离区间RSSI值和真实距离之间映射关系不均匀的问题. 将K-means聚类算法应用在BP神经网络模型中,对样本数据进行距离区间划分,然后将已经分类好的数据分别输入BP神经网络建立网络模型并进行实验仿真. 结果显示,基于BP神经网络的RSSI测距算法的均方根误差为1.425 7 m,而经过K-means算法改进后的BP神经网络测距方法的均方根误差为1.288 7 m,降低了测距误差,并优化了目标RSSI与真实距离的映射关系.
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关键词:
- 路径损耗模型 /
- 接收信号强度指示(RSSI)测距 /
- K-means聚类算法 /
- 反向传播(BP)神经网络 /
- 测距误差 /
- 均方根误差
Abstract: The research of ranging based on received signal strength indication (RSSI) has been a hot spot in the Internet of Things due to its wide application. In order to reduce the traditional RSSI ranging error based on back propagation neural network, a back propagation neural network RSSI ranging method based on K-means clustering algorithm preprocessing sample data is proposed. The algorithm solves the problem of non-uniform mapping between RSSI value and real distance in different distance intervals due to different attenuation degree of RSSI value. The K-means clustering algorithm is applied to BP neural network model to divide the distance interval of the sample data, and then the classified data are input into BP neural network to build the network model and simulate the experiment. The results show that the root mean square error of RSSI ranging method based on BP neural network is 1.4257 m, while the root mean square (RMS) error of BP ranging method neural network improved by K-means algorithm is 1.2887 m. Compared with the existing ranging methods based on BP neural network, it reduces the ranging error and optimizes the mapping relationship between the target RSSI and the real distance. -
表 1 补充后的训练数据
Tab. 1 Supplementary training data
RSSI/dBm 距离/m RSSI/dBm 距离/m −24 1.0 −41 11.5 −25 1.5 −41 12.0 −25 2.0 −42 12.5 −26 2.5 −43 13.0 −27 3.0 −43 13.5 −27 3.5 −43 14.0 −29 4.0 −43 14.5 −30 4.5 −43 15.0 −31 5.0 −44 15.5 −33 5.5 −44 16.0 −33 6.0 −45 16.5 −33 6.5 −46 17.0 −35 7.0 −48 17.5 −36 7.5 −48 18.0 −36 8.0 −49 18.5 −37 8.5 −50 19.0 −38 9.0 −50 19.5 −38 9.5 −51 20.0 −39 10.0 −53 20.5 −40 10.5 −53 21.0 −40 11.0 -
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