基于双神经网络的RFID室内定位方法

在室内定位中，传统的RFID定位方法由于方法简单，无法随着室内环境的变化准确估计当前的路径损耗系数，存在受环境影响大，定位精度不高，实时性差等缺点.为了解决以上问题，提出一种基于双神经网络模型的室内定位算法，建立BP网络和DNN网络的双神经网络模型，将采集到的RSSI信号值预处理后输入到BP网络模型中，输出路径损耗系数n，再将接收信号强度值RSSI和通过BP模型得到的路径损耗系数n作为输入，输入到DNN网络模型中，得到待测标签的精确定位坐标.实验表明，与传统的基于RSSI和基于ANN模型的室内定位算法相比，本算法有效提高了定位精度和定位实时性.

Indoor Position Method for RFID System Based on Dual Neural Network

In the indoor positioning, the traditional RFID positioning method cannot accurately estimate the current path loss coefficient with the change of indoor environment due to its simple method. It has disadvantages such as large environmental impact, low positioning accuracy, and poor real-time performance. In order to solve the above problems, this study puts forward a kind of indoor location algorithm based on dual neural network model, and establishes the BP network and the network within DNN dual neural network model. Then, it preprocesses the collected RSSI signal and inputs the preprocessed signal value to BP network model, outputs path loss coefficient n, and then received signal strength value RSSI and through the BP model to get the path loss coefficient of n as input, input to the network within DNN model, and get the precise positioning of the labels under test coordinates. Experiments show that compared with the traditional indoor positioning algorithm based on RSSI and ANN model, this algorithm effectively improves the positioning accuracy and real-time performance.