GPS: location-tracking technology

Bajaj, R.; Ranaweera, S.L.; Agrawal, D.P.

doi:10.1109/2.993780

Cited by 54 publications

(51 citation statements)

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“…They can be classified into two parts: Indoors and Outdoors localization systems. Outdoor localization can be efficiently accomplished using GPS [1]. However, GPS is difficult to use in indoor settings, due to the lack of satellite signals reception near and inside buildings.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture modeling for indoor positioning WIFI systems

Alfakih

Keche

Benoudnine

2015

2015 3rd International Conference on Control, Engineering &Amp; Information Technology (CEIT)

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Different location determination methods using wireless signal strength have been proposed to improve the location accuracy and mitigate the multipath problem in indoor environment.In this paper, a fingerprinting-probabilistic approach for indoor localization using wireless technology is proposed. The method is based on the use of the Gaussian Mixture Model (GMM) to approximate the probability distribution of the strength of the signal received by a mobile from Access Points (AP). This probability distribution is then used to infer the mobile location. The performance of the proposed method is compared experimentally to that of another powerful method. The comparison shows the effectiveness of the GMM method.

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Section: Introductionmentioning

confidence: 99%

Gaussian mixture modeling for indoor positioning WIFI systems

Alfakih

Keche

Benoudnine

2015

2015 3rd International Conference on Control, Engineering &Amp; Information Technology (CEIT)

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“…Unfortunately, the current current generation of positioning systems provide limited accuracy. For instance, current GPS technology provides accuracy of 3-100 meters [2], while handling moving objects in ultrasound has inherent problems [35]. SEVA must deal with the error that is introduced as a result of these limitations.…”

Section: Research Challengesmentioning

confidence: 99%

“…Street signs, buildings, and popular locations might be equipped with active sensor beacons that electronically broadcast their addresses. Another trend is the ubiquitous deployment of positioning technologies such as GPS [2] and ultrasound [33] that triangulate the exact location of a user. This paper proposes a new multimedia application that is enabled by the confluence of these trends.…”

Section: Introductionmentioning

confidence: 99%

SEVA

Liu

Corner

Shenoy

2005

Proceedings of the 13th Annual ACM International Conference on Multimedia

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In this paper, we study how a sensor-rich world can be exploited by digital recording devices such as cameras and camcorders to improve a user's ability to search through a large repository of image and video files. We design and implement a digital recording system that records identities and locations of objects (as advertised by their sensors) along with visual images (as recorded by a camera). The process, which we refer to as sensor-enhanced video annotation (SEVA), combines a series of correlation, interpolation, and extrapolation techniques. It produces a tagged stream that later can be used to efficiently search for videos or frames containing particular objects or people. We present detailed experiments with a prototype of our system using both stationary and mobile objects as well as GPS and ultrasound. Our experiments show that: (i) SEVA has zero error rates for static objects, except very close to the boundary of the viewable area; (ii) for moving objects or a moving camera, SEVA only misses objects leaving or entering the viewable area by 1-2 frames; (iii) SEVA can scale to 10 fast moving objects using current sensor technology; and (iv) SEVA runs online using relatively inexpensive hardware.

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“…Also,in the work of Pang et al in [3], GPS data were extracted from taxis in order to control the emergence of anomalous behavior in the area of Beijing metropolitan. In addition, GPS is exploited in location tracking [4] field, as well as in medical purposes like the work of Makino et al in [5] to help the blinds... This paper is organized as follows: SectionII presents the used technical instrumentation .…”

Section: Introductionmentioning

confidence: 99%

Mining road map from big database of GPS data

Elleuch

Wali

Alimi

2014

2014 14th International Conference on Hybrid Intelligent Systems

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This paper describes a process of converting raw Global Positioning System (GPS) data to a routable road map. In fact, it is a large scale database collected from thousands of vehicles circulating on Tunisian public roads.Moreover, the paper contains the architecture used to collect GPS data from these vehicles using GPRS connection and all the steps until getting the road traces.The data flow is composed of many steps which are: Collecting data which consists of extracting National Marine Electronics Association(NMEA) sentences; Filtering raw GPS nodes to eliminate outliers and noise caused by several sources of errors; Clustering step, in which we used two methods partitional (k-means)and hierarchical (agglomerative)clustering techniques. We compare them and we choose the most suitable for our work. In fact, K-means algorithm is carried out in order to partition data and facilitate handling the big data sets; Generating a Tunisian map network from our database and map-matching it with Google maps in order to make a comparison between them.

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GPS: location-tracking technology

Cited by 54 publications

References 0 publications

Gaussian mixture modeling for indoor positioning WIFI systems

Gaussian mixture modeling for indoor positioning WIFI systems

SEVA

Mining road map from big database of GPS data

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