结合CNN和WiFi指纹库的室内定位算法

为了提高基于WiFi的室内定位的精度和降低计算时间, 本文提出一种卷积神经网络(Convolutional Neural Networks, CNN)结合传统指纹库的室内定位算法. 该算法基于接收信号强度指示(Received Signal Strength Indication, RSSI)数据, 首先利用卷积神经网络模型, 根据实时输入数据预判出待测点的初步位置. 在保证了大范围预测的位置大概率正确的前提下, 再结合传统指纹库中的指纹点, 确定出精确度更高的最终预测位置. 实验结果表明, 在时效性达到要求的前提下, 累计误差在1 m以内的定位精度约有65%, 累计误差在1.5 m以内的定位精度约有85%, 且误差较为稳定.

Indoor Location Algorithm Combining CNN and WiFi Fingerprint Database

In order to improve the accuracy of WiFi-based indoor positioning and reduce the calculating time, this study proposes an indoor location algorithm combining Convolutional Neural Networks (CNN) with traditional fingerprint library. Based on the Received Signal Strength Indication (RSSI) data, the algorithm first uses the CNN model to predict the initial position of the measured point according to the real-time input data. Under the premise that the large-scale prediction position is guaranteed to be correct, the fingerprint points in the traditional fingerprint database are combined to determine the final prediction position with higher accuracy. The results show that the location accuracy of the error within 1 m is about 65%, the location accuracy of the error within 1.5 m is about 85%, and the error is stable under the premise that the timeliness is required.