2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)最新文献

{"title":"Development of a contextual thinking engine in mobile devices","authors":"Ruizhi Chen, Tianxing Chu, Jingbin Liu, Yuwei Chen, Liang Chen, Wenchao Xu, Xiao Li, J. Hyyppä, Jian Tang","doi":"10.1109/UPINLBS.2014.7033714","DOIUrl":"https://doi.org/10.1109/UPINLBS.2014.7033714","url":null,"abstract":"This paper introduces a framework of contextual thinking in mobile devices. It is based on real-time sensing of local time, significant locations, location-dwelling states, and user states to infer significant activities. A significant activity is a well-defined activity to be inferred, for example, waiting for a bus, having a meeting, working in office, taking a break in a coffee shop et al. A significant location is defined as a geofence, which can be a node associated with a circle, or a polygon. A location-dwelling state is defined as enter into a significant location, the location-dwelling duration, or exit from a significant location. A user state is a combination of user mobility states, user actions, user social states and event psychological states. With this initial study, we just focus on the user motion states including static, slow walking, walking and fast moving that can be fast walking or driving. However, the framework and the activity inference algorithm are flexible for adopting other user states in the future. Using the measurements of the built-in sensors and radio signals in mobile devices, we can capture a snapshot of a contextual tuple for every second, which includes a time tag, an ID of a significant location, a location-dwelling duration, and a user state. The sequence of contextual tuples is used as the inputs for inferring the user significant activities. The contextual thinking engine will evaluate the posteriori probability of each significant activity for each given contextual tuple using a Bayesian approach. An \"un-defined\" activity is adopted to cover all activities other than the selected significant activities. A prototype of the contextual thinking engine has been developed in the Geospatial Computing Lab at Texas A&M University Corpus Christi. A test environment was setup on the campus. Six significant activities were defined and tested by two different testers for three days using two different smartphones. These significant activities include: 1) working in an office; 2) having a meeting; 3) having a lunch, 4) having a coffee break, 5) visiting the library, and 6) waiting for a bus. An \"un-defined\" activity was included to cover all activities other than the selected significant activities. The inferred activities were then compared with the labeled activities to assess the performance of the contextual thinking engine. We demonstrated that the success rate of inference was more than 90% on average. We recognized that the positioning accuracy plays a significant role in the inference algorithm because it has direct impact to two elements in the contextual tuple: the significant location and the location-dwelling duration.","PeriodicalId":133607,"journal":{"name":"2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123130110","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}

{"title":"Fast WiFi access point localization and autonomous crowdsourcing","authors":"Y. Zhuang, B. Wright, Z. Syed, Z. Shen, N. El-Sheimy","doi":"10.1109/UPINLBS.2014.7033737","DOIUrl":"https://doi.org/10.1109/UPINLBS.2014.7033737","url":null,"abstract":"The locations of WiFi access points (APs) are important for WiFi positioning, especially when a propagation model is used. However, AP localization is usually challenging in a new environment because it is hard to obtain parameters for the propagation model, such as the path-loss exponent without the presence of surveyed database. This paper introduces a novel crowdsourcing method for automatic AP localization and propagation parameters (PPs) estimation based on the navigation solution from the Trusted Portable Navigator (T-PN). The estimation for PPs and AP locations based on non-linear weighted least squares (LSQ) is carried out automatically when enough measurements are collected, and the results are recorded in the database for future use. The fast estimation method calculates the propagation parameters autonomously and adaptively to account for the dynamic indoor environment. The autonomous system will also reduce the labour and time costs for the pre-survey and maintenance of databases, as the crowdsourcing is always done in background processes on devices. The accuracy of AP localization is also estimated and recorded in the database, providing an important indicator when using the AP localization results. The performance of the proposed system is evaluated by both simulations and field tests, and the result shows that the average AP localization errors are less than 6 meters.","PeriodicalId":133607,"journal":{"name":"2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130728133","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}

{"title":"Resolving positions of coherent sources using linear antenna arrays at 2.4 GHz","authors":"A. V. Nieuwenhuyse, S. Wielandt, Jean-Pierre Goemaere, B. Nauwelaers, L. D. Strycker","doi":"10.1109/UPINLBS.2014.7033721","DOIUrl":"https://doi.org/10.1109/UPINLBS.2014.7033721","url":null,"abstract":"In this paper we intend to find out what resolution can be achieved with two-dimensional Angle of Arrival localization when using linear antenna arrays at 2.4 GHz. A theoretical resolution calculation method is presented. Room dimensions, number of cooperating anchor nodes (provided with a linear antenna array) which track the position of a mobile node and number of antenna elements can be chosen. These theoretical calculations lead to the definition of a reference value which can be used to calculate the expected resolution for all rectangular shaped rooms with the desired variable settings. It is also shown that square rooms result in the best resolution and adding extra antenna elements improves the resolution. The design and calibration of a practical linear antenna array, with four linearly positioned 2.4 GHz antenna elements and inter distance of λ/2, is presented. Measurements of practical beam patterns, and the corresponding -3dB beam widths, for different incident angles show that influences such as mutual coupling and reflections can not be neglected. The practical resolutions are compared with expected theoretical values and it is shown that besides I/Q and phase offset calibration, manifold calibration is necessary.","PeriodicalId":133607,"journal":{"name":"2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132608873","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}

{"title":"To wireless fidelity and beyond — CAPTURE, extending indoor positioning systems","authors":"G. Cullen, K. Curran, Jose A. Santos, G. Maguire, Denis Bourne","doi":"10.1109/UPINLBS.2014.7033734","DOIUrl":"https://doi.org/10.1109/UPINLBS.2014.7033734","url":null,"abstract":"Most of the current research into indoor localization has surrounded the problem of positioning accuracy, with attempts to solve this using a myriad of technologies and algorithms. One of the problems that seems to be somewhat overlooked is the issue of coverage in an indoor localization solution. The mostly unobstructed views of the Global Positioning System (GPS) which requires a mere 30 satellites to provide global coverage never had these problems. Unfortunately unobstructed views are not something that can be achieved in most indoor environments and economical as well as physical barriers can prevent the installation of an infrastructure to achieve total coverage. In this paper we propose a solution to this issue of indoor coverage by deploying a solution to extend the range of a positioning system - Cooperatively Applied Positioning Techniques Utilizing Range Extension (CAPTURE). CAPTURE provides a system to locate devices that cannot be reached by an in-house location based system. It presents a unique contribution to research in this field by offering the ability to utilize devices that currently know their location within a Location Based Solution (LBS), to help evaluate the position of unknown devices beyond the range capacity of the LBS. Effectively extending the locating distances of an Indoor LBS by utilizing the existing mobile infrastructure without the requirement for additional hardware. CAPTURE uses the Bluetooth radios on mobile devices to estimate the distance between devices, before inserting these range estimates into a trilateration algorithm to ascertain position. CAPTURE has been tested through experiments carried out in a real world environment, proving the capacity to provide a solution to the ranging issue.","PeriodicalId":133607,"journal":{"name":"2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130202579","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}

{"title":"Ariadne's thread: Robust turn detection for path back-tracing using the iPhone","authors":"German H. Flores, R. Manduchi, Enrique D. Zenteno","doi":"10.1109/UPINLBS.2014.7033720","DOIUrl":"https://doi.org/10.1109/UPINLBS.2014.7033720","url":null,"abstract":"Most systems for pedestrian localization and self-tracking aim to measure the precise position of the walker and match it against a map of the environment. In some cases, a simpler topological description of the path taken may suffice. This is the case for the system described in this paper, which is designed to help a blind person re-trace the route taken inside a building and to walk safely back to the starting point. We present two turn detection algorithms based on hidden Markov models (HMM), which process inertial data collected by an iPhone kept in the walker's front pocket, without the need for a map of the environment. Quantitative results show the robustness of the proposed turn detectors even in the case of drift in the measurements and noticeable body sway during gait.","PeriodicalId":133607,"journal":{"name":"2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121594706","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}