2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)最新文献_第2页

A Dynamic Adaptive Positioning Method based on Differential Signal Feature Map 基于差分信号特征映射的动态自适应定位方法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559856

Z. Deng, Wen Liu, Jiaoji Chao, Xiaohan Yan, L. Lin, Wei Wang

{"title":"A Dynamic Adaptive Positioning Method based on Differential Signal Feature Map","authors":"Z. Deng, Wen Liu, Jiaoji Chao, Xiaohan Yan, L. Lin, Wei Wang","doi":"10.1109/UPINLBS.2018.8559856","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559856","url":null,"abstract":"This paper proposes a dynamic adaptive positioning method based on differential signal feature map of cellular network which aims at how to accurately calculate the non-line-of-sight(NLOS) error in dynamic environment. The main innovations include the accurate compensation method of NLOS error based on machine learning and the dynamic adaptive updating method of the differential signal feature map. We build the differential signal feature map and the NLOS error distribution map based on the signal feature after establishing the 3D geographical environment by wireless insite fully considering physical and architectural features. We proposed TD-GA-BPNN algorithm to train the model related to the differential signal characteristics and NLOS distribution as the NLOS compensation method. While the dynamic adaptive updating method adopts a four branch tree partition technique which can dynamically update the feature map by the information of the sparse virtual stations. Through experimental verification, this method eventually achieve high-precision accuracy which can be less than 2m in the actual dynamic environment.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126761885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Improved Clustering Algorithm of Neighboring Reference Points Based on KNN for Indoor Localization 基于KNN的室内定位邻近参考点聚类改进算法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559874

Weixing Xue, Xianghong Hua, Qingquan Li, W. Qiu, Xuesheng Peng

{"title":"Improved Clustering Algorithm of Neighboring Reference Points Based on KNN for Indoor Localization","authors":"Weixing Xue, Xianghong Hua, Qingquan Li, W. Qiu, Xuesheng Peng","doi":"10.1109/UPINLBS.2018.8559874","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559874","url":null,"abstract":"Reference points (RP) clustering methods such as K-means are frequently used to reduce the region of search in most of fingerprint clustering algorithms. However, traditional clustering algorithms analysis the geometric proximity of RP only in the off line phase, which has nothing to do with the test point. Meanwhile, both the clustering pattern and the number of clusters need to be predefined directly or indirectly, which means an unsuitable clustering pattern or an unsuitable number of clusters would lead to poor estimation accuracy. In this letter, in order to improve the performance of KNN algorithm with the neighboring RP selection, we utilize the k-means clustering algorithm to analysis the geometric proximity between RP and test point in the online phase. In the proposed algorithm, k-means clustering algorithm groups M(M<K) nearest neighboring RPs according to their real physical distances to the test point instead of their signal distances. Then these M nearest neighboring RPs are used to estimate the location of the test point. Experiments were conducted in the fourteenth floor within an office building and the results demonstrate that the proposed method considerably outperforms the KNN algorithms in terms of positioning accuracy.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Fusing the Data of Dual Foot-Mounted INS Based on a Heuristic Equality Constraint 基于启发式等式约束的双足式惯性导航系统数据融合

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559758

X. Niu, Yu Li, Jian Kuang, Peng Zhang

引用次数: 0

Robust Sparse Overlapping Group Lasso for indoor localization 鲁棒稀疏重叠群套索室内定位

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559930

Gang Yang, Xinquan Ye

引用次数: 1

An Indoor Floor Positioning Method Based on Smartphone’s Barometer 基于智能手机气压计的室内地板定位方法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559769

Chao Ruan, Min Yu, Xiaona He, Binbin Song

引用次数: 3

PILC: Passive Indoor Localization Based on Convolutional Neural Networks 基于卷积神经网络的被动室内定位

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559775

Chenwei Cai, L. Deng, Mingyang Zheng, Shufang Li

{"title":"PILC: Passive Indoor Localization Based on Convolutional Neural Networks","authors":"Chenwei Cai, L. Deng, Mingyang Zheng, Shufang Li","doi":"10.1109/UPINLBS.2018.8559775","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559775","url":null,"abstract":"Nowadays, an increasing number of location-based services are included in security systems and mobile applications. Various localization mechanisms have been developed, including outdoor satellite navigation system and indoor received signal strength (RSS) based on WiFi and BLE Beacon. However, they can only localize the target while being equipped with a receiver. Instead, passive indoor localization is the device-free technique that can localize a target without carrying any electronic devices in a selected region. In this paper, a scheme for operating passive indoor localization is proposed. In the scheme, the location images are constructed by utilizing channel state information (CSI) while the localization model is built and trained by using deep learning. With the help of convolutional neural networks (CNN), this scheme only requires original CSI amplitude instead of manual extraction of features. We demonstrate the accuracy of this scheme in two typical indoor scenarios. The experimental results show that the proposed scheme achieves an accuracy of more than an average of 94% and 96% respectively in the scenario of the office and the corridory.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121390332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

A Novel PDR Aided UWB Indoor Positioning Method 一种新的PDR辅助超宽带室内定位方法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559706

Ming Xia, C. Xiu, Dongkai Yang, Li Wang

{"title":"A Novel PDR Aided UWB Indoor Positioning Method","authors":"Ming Xia, C. Xiu, Dongkai Yang, Li Wang","doi":"10.1109/UPINLBS.2018.8559706","DOIUrl":"https://doi.org/10.1109/UPINLBS.2018.8559706","url":null,"abstract":"Ultra-wide Band (UWB) plays an increasingly important role in indoor positioning due to its advantages of low emission power, high multipath resolution, and centimeter level positioning accuracy. However, the UWB system is vulnerable to Non-Line of Sight (NLOS) because of human or vehicle occlusion. In this paper, a novel Pedestrian Dead Reckoning (PDR) aided UWB indoor positioning method is proposed. Firstly, an NLOS accuracy factor model is constructed according to the distance difference between UWB and PDR. This accuracy factor judges whether UWB works normally or not. Once the accuracy factor exceeds a certain threshold, PDR positioning is adopted. Meanwhile, the direction and the position predicted by the extended extrapolation method from UWB are applied to provide the initial value for PDR processing. If the accuracy factor is within the threshold range, PDR is replaced by UWB. The experimental results show that the method proposed improves the positioning accuracy of UWB, and that the generalized extrapolation model can calculate more reliable initial values before PDR operates. The obtained results have validated that the proposed method can effectively restrain the impact of NLOS on stable and accurate positioning.","PeriodicalId":367462,"journal":{"name":"2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123852721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

An Indoor Positioning Research Based On The Least Square Method Of Monte Carlo 基于蒙特卡罗最小二乘法的室内定位研究

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559944

Lingna Li, Jingshi Shi, Yu-Chol Kang, Jing-Pu Duan, P. Sun

引用次数: 1

A Geocoding Framework for Indoor Navigation based on the QR Code 基于QR码的室内导航地理编码框架

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559741

Ying Zhuang, Y. Kang, Lina Huang, Z. Fang

引用次数: 6

A fast initialization method of Visual-Inertial Odometry based on monocular camera 基于单目摄像机的视觉惯性里程计快速初始化方法

2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) Pub Date : 2018-03-22 DOI: 10.1109/UPINLBS.2018.8559929

Lixiao Huang, S. Pan, Shuai Wang, Pan Zeng, Fei Ye

引用次数: 1