姚军,甄梓越,马宇静. 基于BP神经网络的RSSI测距优化算法[J]. 电波科学学报,2022,37(4):663-669. DOI: 10.12265/j.cjors.2021177
      引用本文: 姚军,甄梓越,马宇静. 基于BP神经网络的RSSI测距优化算法[J]. 电波科学学报,2022,37(4):663-669. DOI: 10.12265/j.cjors.2021177
      YAO J, ZHEN Z Y, MA Y J. RSSI ranging optimization algorithm based on BP neural network[J]. Chinese journal of radio science,2022,37(4):663-669. (in Chinese). DOI: 10.12265/j.cjors.2021177
      Citation: YAO J, ZHEN Z Y, MA Y J. RSSI ranging optimization algorithm based on BP neural network[J]. Chinese journal of radio science,2022,37(4):663-669. (in Chinese). DOI: 10.12265/j.cjors.2021177

      基于BP神经网络的RSSI测距优化算法

      RSSI ranging optimization algorithm based on BP neural network

      • 摘要: 基于接收信号强度指示(received signal strength indication, RSSI)测距的研究和应用领域很广泛,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神经网络测距算法,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聚类算法应用于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:传统基于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神经网络测距算法的均方根误差为1.288 7 m,降低了测距误差,并优化了目标RSSI值与真实距离的映射关系.

         

        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 the traditional 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.

         

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