基于cGAN-SAE的室内定位指纹生成方法  

Indoor positioning fingerprint generation method based on cGAN-SAE

作　　者：刘伟[1,3] 王智豪 李卓 韦嘉恒 Liu Wei;Wang Zhihao;Li Zhuo;Wei Jiaheng(School of Physics and Electronic Information Engineering,Guilin University of Technology,Guilin 541006,China;School of Computer Science and Engineering,Guilin University of Technology,Guilin 541006,China;Guangxi Key Laboratory of Embedded Technology and Intelligent Systems,Guilin University of Technology,Guilin 541006,China)

机构地区：[1]桂林理工大学物理与电子信息工程学院,桂林541006 [2]桂林理工大学计算机科学与工程学院,桂林541006 [3]桂林理工大学广西嵌入式技术与智能系统重点实验室,桂林541006

出　　处：《电子测量技术》2024年第14期57-63,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(62362017)项目资助。

摘　　要：针对室内定位中指纹采集成本高、构建数据集难等问题,提出了一种基于条件稀疏自编码生成对抗网络的室内定位指纹生成方法。该方法通过增加自编码器隐藏层和输出层,增强了特征提取能力,引导生成器学习并生成指纹数据的关键特征。利用指纹选择算法筛选出最相关的指纹数据,扩充至指纹数据库中,并用于训练卷积长短时记忆网络模型以进行在线效果评估。实验结果表明,条件稀疏自编码生成对抗网络在不增加采集样本的情况下,提高了多栋多层建筑室内定位的精度。与原始条件生成对抗网络模型相比,在UJIIndoorLoc数据集上的预测中,定位误差降低了6%;在实际应用中,定位误差降低了14%。To address the issues of high fingerprint collection costs and the difficulty of constructing datasets in indoor positioning,a method for indoor positioning fingerprint generation based on a conditional sparse autoencoder generative adversarial network is proposed.This method enhances the feature extraction capability by adding hidden and output layers to the autoencoder,guiding the generator to learn and generate key features of fingerprint data.A fingerprint selection algorithm is used to filter out the most relevant fingerprint data,which is then added to the fingerprint database and used to train a convolutional long short-term memory network model for online performance evaluation.Experimental results show that the conditional sparse autoencoder generative adversarial network improves the accuracy of indoor positioning in multi-building,multi-floor environments without increasing the number of collected samples.Compared to the original conditional generative adversarial network model,the positioning error in predictions on the UJIIndoorLoc dataset is reduced by 6%,and in practical applications,the positioning error is reduced by 14%.

关 键 词：室内定位 稀疏自编码器 指纹数据库 条件生成对抗网络 卷积长短时记忆网络 

分 类 号：TN92[电子电信—通信与信息系统]