摘要： 针对基于测距模型的定位算法易受环境干扰、测距误差大的问题，提出一种基于遗传算法-广义回归神经网络（GA-GRNN）优化的指纹定位算法。利用GRNN建立节点定位模型，通过GA确定最优平滑参数，将阅读器与标签间的信号强度值作为神经网络的输入，进而得到输出节点的坐标。仿真结果表明，与GRNN算法、BP神经网络算法、FOA-GRNN算法相比，该算法的定位精度较高，泛化能力较强。

关键词: 接收信号强度指示, 射频识别, 广义回归神经网络, 室内定位, 遗传算法优化

Abstract: To address the problem that the positioning algorithm based on distance measuring is vulnerable to environmental interference and large measurement errors,a fingerprint positioning algorithm based on Genetic Algorithm-Generalized Regression Neural Networks(GA-GRNN) is proposed.The GRNN is used to establish the node positioning model.The GA is used to determine the optimal smoothing parameters of the generalized neural network.The signal strength value between the reader and the tag is used as the input of the neural network,and the output node coordinates are obtained.The simulation results show that compared with GRNN algorithm,BP neural network algorithm and FOA-GRNN algorithm,the algorithm has higher positioning accuracy and strong generalization ability.

Key words: Received Signal Strength Indication(RSSI), Radio Frequency Identification(RFID), Generalized Regression Neural Networks(GRNN), indoor positioning, Genetic Algorithm(GA) optimization

中图分类号: 

• TP391