CellSense: A Probabilistic RSSI-Based GSM Positioning System

Ibrahim, Mohamed; Youssef, Moustafa

doi:10.1109/glocom.2010.5683779

Cited by 69 publications

(39 citation statements)

References 10 publications

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“…In previous GSM location based technique [1],they proposed a probabilistic fingerprinting based technique for GSM localization. The CellSense probabilistic technique provides more accurate localization.…”

Section: Existing Systemmentioning

confidence: 99%

Geolocalization Techniques using Log Files in Android

Ramakrishnaiah¹,

Sandeep²

2013

IJCA

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The location-sensing technology is core technology for location based services and applications. In these days Localization for mobile handset devices are gaining huge importance in various fields, they are globally useful, such as navigation, advertisements, safety security and also observation of tracking. This paper presents various techniques for location-sensing method, called a cell Id based location positioning method and its positioning accuracy estimation. In addition this paper proposed Geolocation technique which is used to track a specific user without permissions. This includes tracking, reading call history, past location history, reading SMS. To evaluate proposed system this paper implemented location-sensing technique on Android-based phones. There are different ways how information about the location of a mobile device is generated. This paper explains the state of art technology of WiFi and Cell Id based positioning and describes some of its applications. By knowing how location data is collected and processed the reader understands how location-based solutions work. Some examples are given at the end of the paper.

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Section: Existing Systemmentioning

confidence: 99%

Geolocalization Techniques using Log Files in Android

Ramakrishnaiah¹,

Sandeep²

2013

IJCA

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“…Localization of RF sources could be classified based on the measured signal parameters including RSS [5], differential RSS (DRSS) [6,7], angle of arrival [8,9], time of arrival [10][11][12], time difference of arrival [13,14], and so on. Simplicity in hardware, implementation, and computation of RSS-based localization makes it preferable in variety of applications including localization in sensor networks [15] and location-based services in cellular networks [16] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Single- and Multi-UAV Trajectory Control in RF Source Localization

Shahidian

Soltanizadeh

2016

Arab J Sci Eng

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In this paper, two trajectory control approaches are presented for any number of unmanned aerial vehicles (UAVs) in radio frequency (RF) source localization. The UAVs observe the received signal strength (RSS) in distinctive time intervals to localize a stationary RF source. The location of the source is estimated recursively applying the extended Kalman filter. The objective of the optimal trajectory control is to steer the UAVs to the locations which minimize the uncertainty about the target state. The Fisher information matrix (FIM) is inversely proportional to the estimation variance. Since the true target state is unknown, the FIM is approximated by the estimated target state. Two criteria based on the approximated FIM are applied to measure the information content of the observations to optimize the UAV waypoints: The D-optimality and the A-optimality. The objective of the present paper is to propose two trajectory control approaches for any number of UAVs in RSS-based localization to increase the target localization accuracy. The superiority of the trajectory optimization approach based on the D-optimality in terms of mean squared error is illustrated through simulation examples.

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“…The grid-based methods [8,10] generally divide the simulation environment into uniform regular grids and then attempt to map a MS location to a point on a grid element. The spacing of the grid influences the accuracy of the position estimation [4].…”

Section: Partitioning Modelsmentioning

confidence: 99%

“…However, previous fingerprint studies [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] mainly focus on indoor localisation due to the difficulty in acquiring the large amount of data that need to be processed for larger outdoor areas.…”

Section: Introductionmentioning

confidence: 99%

Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting

Ning

2016

Wireless Pers Commun

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A cluster-based intersection fingerprinting technique for outdoor location estimation using received signal strength (RSS) is proposed. The performance of the proposed scheme is demonstrated by making comparisons using RSS data from a simulated grid-based urban propagation model, RSS data generated by a network planning tool applied to a rural environment, and RSS data from real city environment. The proposed scheme first uses an optimal clustering scheme to portion the large outside area into different small regions based on the use of RSS deviations from the path loss model. For each region, a fine RSS distribution model is built to provide a good support for further positioning. An improved intersection method is then presented to find the most likely geographical area to further estimate a mobile user's location. A comparison between cluster-based and grid-based environment partitioning is made. The experimental results show that the proposed clustering scheme gives good support for location estimation and the positioning accuracy is significantly improved.

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CellSense: A Probabilistic RSSI-Based GSM Positioning System

Cited by 69 publications

References 10 publications

Geolocalization Techniques using Log Files in Android

Geolocalization Techniques using Log Files in Android

Single- and Multi-UAV Trajectory Control in RF Source Localization

Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting

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