[1]尹铁源,刘 祺.基于卷积神经网络的室内定位方法的研究[J].计算机技术与发展,2021,31(增刊):31-35.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 006]
　YIN Tie-yuan,LIU Qi.Study on Indoor Localization Method Based on Convolutional Neural Network[J].,2021,31(增刊):31-35.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 006]
点击复制

基于卷积神经网络的室内定位方法的研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年增刊
页码:
31-35
栏目:
人工智能
出版日期:
2021-12-31

文章信息/Info

Title:
Study on Indoor Localization Method Based on Convolutional Neural Network
文章编号:
1673-629X(2021)S0031-05
作者:
尹铁源; 刘 祺
沈阳工业大学 信息科学与工程学院,辽宁 沈阳 110870
Author(s):
YIN Tie-yuan; LIU Qi
School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China
关键词:
位置指纹; 室内定位; 卷积神经网络; 定位技术
Keywords:
location fingerprint; indoor localization; convolutional neural networks; localization techniques
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2021. S. 006
摘要:
随着物联网的不断发展,基于位置的服务已经成为了当今世界最为迫切的需求之一。 由于室内环境的复杂性,指纹定位方法在室内定位中有着优越的性能,但当前的指纹定位技术仍有许多问题和挑战需要解决。 针对传统的指纹定位算法中存在的复杂室内场景定位精度差和信号不稳定的问题,提出了一种基于卷积神经网络的室内定位的算法。 该算法首先对采集的数据进行预处理,其次利用一大部分数据作为训练集通过一维卷积训练得出神经网络模型,再次利用剩余的小部分数据作为测试集对模型进行测试,最终通过对比测试结果和实际结果得出误差。 并将误差与传统的室内定位方法进行比较。 实验表明,基于卷积神经网络的室内定位方法与传统的室内定位方法相比,精度和鲁棒性都有显著的提高。
Abstract:
With the continuous development of the Internet of Things,location-based services have become one of the most urgent needsin today’s world. Due to the complexity of indoor environment, fingerprint location method has excellent performance in indoorlocation,but there are still many problems and challenges to be solved in the current fingerprint location technology. In order to solve the problems of poor positioning accuracy and signal instability in traditional fingerprint location algorithms,we propose an indoor location algorithm based on convolutional neural network. The algorithm first preprocesses the collected data,then uses a large part of the data asthe training set to obtain the neural network model through one-dimensional convolution training,and then uses the remaining small part of the data as the test set to test the model,and finally obtains the error by comparing the test results with the actual results. The error is compared with the traditional indoor positioning method. The experiment shows that the accuracy and robustness of the indoor positioning method based on convolutional neural network are significantly improved compared with the traditional indoor positioning method.

相似文献/References:

[1]刘君 吴建国 褚曦丹 朱丽进 李炜.Cricket室内定位系统的研究与改进[J].计算机技术与发展,2011,(05):206.
　LIU Jun,WU Jian-guo,CHU Xi-dan,et al.Research and Improvement of Cricket Indoor Location System[J].,2011,(增刊):206.
[2]玄建永,王京春,陆耿,等.缩微智能车室内定位系统研究[J].计算机技术与发展,2014,24(01):1.
　XUAN Jian-yong,WANG Jing-chun,LU Geng,et al.Research on Indoor Position System for Autonomous Micro-vehicle[J].,2014,24(增刊):1.
[3]王凡,彭勇.基于TDOA的室内超声波定位方法的改进[J].计算机技术与发展,2014,24(06):250.
　WANG Fan,PENG Yong.Improved Indoor Location Method for Ultrasonic System Based on TDOA[J].,2014,24(增刊):250.
[4]杜月林[],石欣然[],王克寒[]. 基于ZigBee技术的室内定位系统算法研究及实现[J].计算机技术与发展,2014,24(07):245.
　DU Yue-lin[],SHI Xin-ran[],WANG Ke-han[]. Research and Realization of Indoor Positioning System Algorithm Based on ZigBee[J].,2014,24(增刊):245.
[5]玲玉,杨恒新,张昀. 基于无源UHF RFID的一种室内定位算法[J].计算机技术与发展,2015,25(06):110.
　QU Ling-yu,YANG Heng-xin,ZHANG Yun. An Indoor Location Algorithm Based on Passive UHF RFID[J].,2015,25(增刊):110.
[6]王语琪[][],巩应奎[]. 一种基于视觉信息的可见光通信室内定位方法[J].计算机技术与发展,2016,26(01):200.
　WANG Yu-qi[][],GONG Ying-kui[]. An Indoor Positioning Method of Visible Light Communication Based on Visual Information[J].,2016,26(增刊):200.
[7]程俊[],周礼争[],余敏[],等. 基于RSSI滤波的改进型泰勒级数室内定位算法[J].计算机技术与发展,2016,26(05):51.
　CHENG Jun[],ZHOU Li-zheng[],YU Min[],et al. Indoor Localization Algorithm of Improved Taylor Series Based on RSSI Filter[J].,2016,26(增刊):51.
[8]程海鸣[],黄玲[],徐鹤[],等. 基于RFID的图书馆书籍管理系统设计与实现[J].计算机技术与发展,2016,26(10):99.
　CHENG Hai-ming[],HUANG Ling[],XU He[],et al. Design and Implementation of Library Management System Based on RFID[J].,2016,26(增刊):99.
[9]王丽园[],吴沐阳[],吴家皋[][]. 基于WIFI BSSID相似度和RSSI概率分布的定位算法[J].计算机技术与发展,2016,26(12):200.
　WANG Li-yuan[],WU Mu-yang[],WU Jia-gao[][]. A Positioning Algorithm Based on WIFI BSSID Similarity and RSSI Probability Distribution[J].,2016,26(增刊):200.
[10]王田芳[],张玲华[].基于模糊分类和AP加权的室内定位算法[J].计算机技术与发展,2017,27(04):46.
　WANG Tian-fang[],ZHANG Ling-hua[]. Indoor Localization Algorithm with Fuzzy Classification and AP Weighting[J].,2017,27(增刊):46.
[11]陶峥[] ],王洪玉[]. 基于卡方距离改进的WLAN室内定位算法[J].计算机技术与发展,2016,26(09):50.
　TAO Zheng[][],WANG Hong-yu[]. Improved WLAN Localization Algorithm Based on Chi-square Distance[J].,2016,26(增刊):50.

更新日期/Last Update: 2021-09-10