5,179 research outputs found

    {WiFi GPS} based Combined positioning Algorithm

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    International audienceIf nowadays, positioning becomes more and more accurate, and covers better and better a territory (indoor and outdoor), it remains territories where traditional (and basic) positioning system (GPS, gsm or WiFi) and hybrid ones (GPS-gsm, GPS-WiFi, GPS-WiFi-gsm,...) are insufficient and requires research investment treating combined positioning. In this paper we propose a GPS-WiFi combined positioning algorithm, based on trilateration technique. Real experiments and other simulation are conduced and demonstrate accuracy gains, even where various criteria dilution of precision (GPS dop s criteria, or ours WiFi geometrical and signal attenuation s dop proposal, or hybrid dop one s) indicate all the disruption of positioning service. A testbed scenario issued from a real urban campus environment validates not only our GPS-WiFi combined positioning algorithm but also an implementation of pertinent positioning techniques and dop s criteria. This work constitutes ?a further ?step to better position everywhere and to ensure continuity of a positioning service

    Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI

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    We present a low complexity experimental RF-based indoor localization system based on the collection and processing of WiFi RSSI signals and processing using a RSS-based multi-lateration algorithm to determine a robotic mobile node's location. We use a real indoor wireless testbed called w-iLab.t that is deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this testbed is that it provides tools and interfaces using Global Environment for Network Innovations (GENI) project to easily create reproducible wireless network experiments in a controlled environment. We provide a low complexity algorithm to estimate the location of the mobile robots in the indoor environment. In addition, we provide a comparison between some of our collected measurements with their corresponding location estimation and the actual robot location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

    An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

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    With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

    Tracking Human Mobility using WiFi signals

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    We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking

    Navigating MazeMap: indoor human mobility, spatio-logical ties and future potential

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    Global navigation systems and location-based services have found their way into our daily lives. Recently, indoor positioning techniques have also been proposed, and there are several live or trial systems already operating. In this paper, we present insights from MazeMap, the first live indoor/outdoor positioning and navigation system deployed at a large university campus in Norway. Our main contribution is a measurement case study; we show the spatial and temporal distribution of MazeMap geo-location and wayfinding requests, construct the aggregated human mobility map of the campus and find strong logical ties between different locations. On one hand, our findings are specific to the venue; on the other hand, the nature of available data and insights coupled with our discussion on potential usage scenarios for indoor positioning and location-based services predict a successful future for these systems and applications.Comment: 6 pages, accepted at PerMoby Workshop at IEEE PerCom 201

    A Survey of Positioning Systems Using Visible LED Lights

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    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

    Distributed and adaptive location identification system for mobile devices

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    Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments
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