【摘要】 针对传统神经网络算法定位误差大和单一地磁技术存在的不足，以及其他室内定位方法存在的缺点，本文提出了一种基于长短期记忆递归神经网络(LSTM)与地磁、光照强度、小波降噪技术相融合的方法。离线阶段将坐标轴转换的地磁三轴数据与对应光照强度数据结合提升定位点的特征维度，采用小波降噪的方法对数据降噪并建立指纹库，输入到LSTM神经网络模型，建立地磁定位模型。在线阶段利用已建立的地磁定位模型输出定位结果。实验结果表明，平均误差比降噪前的平均误差减小了17%,该融合定位方法平均误差相比单一地磁定位技术提升了34.5%,该定位模型具有较好的定位性能，可有效应用于室内定位；且地磁/光强/小波降噪融合室内定位技术可明显提升定位精度及稳定性，解决复杂环境下的定位精度问题。更多还原

【Abstract】 In view of the large positioning error of traditional neural network algorithm and the shortcomings of single geomagnetic technology, as well as the shortcomings of other indoor positioning methods, this paper proposes a method based on long short-term memory recurrent neural network and geomagnetic, light intensity and wavelet noise reduction technology. In the off-line stage, the three-axis geomagnetic data converted by coordinate axes and the corresponding light intensity data are combined to improve the feature dimension of the positioning point. The wavelet denoising method is adopted to denoise the data and establish a fingerprint database, which is input into the LSTM neural network model to establish the geomagnetic positioning model. In the online stage, the established geomagnetic positioning model is used to output the positioning results. The experimental results show that the average error of the fusion positioning method is reduced by 17% compared with the average error before noise reduction, and the average error of the fusion positioning method is increased by 34.5% compared with the single geomagnetic positioning technology. The positioning model has good positioning performance and can be effectively applied to indoor positioning. Moreover, the indoor positioning technology of geomagnetic/light intensity/wavelet noise reduction can significantly improve the positioning accuracy and stability, and solve the positioning accuracy problem in complex environments.更多还原