Database correlation method for GSM location

Laitinen, Heikki; Lähteenmäki, Jaakko; Nordström, Tero

doi:10.1109/vetecs.2001.944052

Cited by 150 publications

(89 citation statements)

References 2 publications

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“…These previous efforts to use GSM for localization differ from the work reported in this paper in that they used narrow fingerprints that include the signal strength for the current cell [12,15] or the 6-strongest cells [14]. In contrast, we used wide fingerprints that include up to 29 different GSM channels in addition to the 6-strongest GSM cells, which significantly improve localization accuracy.…”

Section: Localizing Using Gsmmentioning

confidence: 97%

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Accurate GSM Indoor Localization

Otsason

Varshavsky

LaMarca

et al. 2005

Lecture Notes in Computer Science

331

207

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Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median within floor accuracy of 4 m in large buildings and is able to identify the floor correctly in up to 60% of the cases and is within 2 floors in up to 98% of the cases in tall multi-floor buildings. We report evaluation results of two case studies conducted over a course of several years, with data collected from 6 buildings in 3 cities across North America. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signalstrength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but are too weak to be used for efficient communication. We further show that selecting a subset of highly relevant channels for fingerprinting matching out of all available channels, further improves the localization accuracy.

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Section: Localizing Using Gsmmentioning

confidence: 97%

“…Laitinen et al [14] used GSM-based fingerprinting for outdoor localization. They have collected sparse fingerprints from the 6-strongest cells, achieving 67th percentile accuracy of 44 m. Finally, Laasonen et al used the transition between GSM cell towers to build a graph representing the places a user goes [12].…”

Section: Localizing Using Gsmmentioning

confidence: 99%

Accurate GSM Indoor Localization

Otsason

Varshavsky

LaMarca

et al. 2005

Lecture Notes in Computer Science

331

207

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“…The other very popular direction in positioning is utilisation of fingerprinting algorithms, such as Database Correlation Method (DCM) (Laitinen et al, 2001) and Artificial Neural Networks (ANN) (Hassoun, 1995). These algorithms require databases with measured or simulated data to be created prior to the position estimation.…”

Section: R E L At E D W O R Kmentioning

confidence: 99%

“…Regarding data correlation algorithms, the most often used are the Nearest Neighbour (NN), k Nearest Neighbours (kNN) and Weighted k Nearest Neighbours (WkNN) algorithms (Bhatia and Vandana, 2010). A DCM model constructed from field RSS measurements achieved an accuracy of 74 m|67% in suburban and 44 m|67% in urban environments, using the NN algorithm (Laitinen et al, 2001). A WkNN-based model achieved average positioning error of 112 m in a real urban environment (Lakmali et al, 2007).…”

Section: R E L At E D W O R Kmentioning

confidence: 99%

“…To credibly verify the performances of the proposed SVR, SVC&SVR 2 × 2 and SVC&SVR 4 × 4 models, we have applied the exact same field data to some of the most promising techniques from Section 2: SVR with SoE kernel function -SVR SoE (Wu et al, 2007), DCM (Laitinen et al, 2001), ANN (Takenga et al, 2006) and EKF trained ANN (Anne et al, 2004). SVR SoE was chosen in order to credibly verify the proposed approach for handling missing RSS values in the database.…”

Section: S V M -B a S E D M O D E L S Fo R Po S I T I O N I N G Nomentioning

confidence: 99%

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SVM-based Models for Mobile Users' Initial Position Determination

Petric

Nešković

et al. 2014

J. Navigation

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A large interest in developing commercial Location-Based Services (LBS) and the necessity of implementing emergency call services, have led to the intensive development of techniques for mobile users' localisation. In this paper, a Public Land Mobile Networks (PLMN) -based technique for initial position determination is proposed as an alternative to satellite-based methods in environments with obstructed satellite signals. Two positioning models, based on handset available Received Signal Strength (RSS) measurements from Global System for Mobile Communications (GSM) base stations and the use of Support Vector Machine (SVM) algorithms, are proposed. Performances of proposed models are verified using field measurements, collected in a suburban environment. Models are analysed in terms of positioning accuracy, complexity and latency, and compared to some other promising PLMN-based techniques. Using proposed SVM-based positioning models a median error of 4·3 m-6·2 m and latency of less than a second can be achieved. K E Y

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Multi‐system‐multi‐operator localization in PLMN using neural networks

Borenovic

Nešković

Budimir

2011

Int J Communication

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SUMMARY Providing the localization algorithm for context‐aware services is the focus of many studies. This paper explores the properties of positioning models based on received signal strength (RSS) in PLMN (Public Land Mobile Network) networks. The effects of using the RSS at a mobile terminal from various systems, such as GSM and UMTS, as well as from multiple operators, have been investigated and discussed. Twenty‐two models, based on artificial neural networks, have been developed and verified using the data from an immense measurement campaign. The obtained results show that augmenting the model with additional RSS data, even from systems with poor radio‐visibility, may improve the positioning accuracy to as much as a 35thinspacem median distance error in a light urban environment. The degradation of accuracy in indoor environments and the complexity and latency of the models were also scrutinized. Copyright © 2011 John Wiley & Sons, Ltd.

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Database correlation method for GSM location

Cited by 150 publications

References 2 publications

Accurate GSM Indoor Localization

Accurate GSM Indoor Localization

SVM-based Models for Mobile Users' Initial Position Determination

Multi‐system‐multi‐operator localization in PLMN using neural networks

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