Database correlation method for UMTS location

Ahonen, Suvi; Laitinen, Heikki

doi:10.1109/vetecs.2003.1208882

Cited by 35 publications

(24 citation statements)

References 5 publications

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“…In Figures 5, 6, and 7, TAE was calculated by averaging the best 10% weighted location candidates, that is, k = 0.1 * n in (15). The explanation in the previous paragraph can be confirmed if we look at the results obtained when k is increased up to 0.9 * n. Figure 11: Mean positioning error of the location estimation algorithm using WAE and TAE (k = 0.6 * n-0.9 * n).…”

Section: Location Estimation Resultsmentioning

confidence: 99%

“…Nevertheless field measurements in some parts of the deployment environment do help to show the performance upper limit of location estimation algorithms using the mapping approach. Location-dependent parameters usually used for mapping include received signal strength [12,14,15] which is the multipath propagation delay profile of the environment. In GSM systems, the bandwidth is too small, unlike the UMTS system, for accurate positioning based on correlation of CIR only [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonparametric Bayesian Filtering for Location Estimation, Position Tracking, and Global Localization of Mobile Terminals in Outdoor Wireless Environments

Khalaf-Allah

2007

EURASIP J. Adv. Signal Process.

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The mobile terminal positioning problem is categorized into three different types according to the availability of (1) initial accurate location information and (2) motion measurement data. Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available, the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework. Filter derivation and implementation algorithms are provided with emphasis on the mapping approach. The radio maps of the experimental area have been created by a 3D deterministic radio propagation tool with a grid resolution of 5 m. Real-world experimentation was conducted in a GSM network deployed in a semiurban environment in order to investigate the performance of the different positioning algorithms.

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Section: Location Estimation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonparametric Bayesian Filtering for Location Estimation, Position Tracking, and Global Localization of Mobile Terminals in Outdoor Wireless Environments

Khalaf-Allah

2007

EURASIP J. Adv. Signal Process.

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“…Moreover, they can be used in many situations where the A-GPS method cannot be applied, i.e., indoor positioning, but generally with a degradation in accuracy. So far, a wide variety of network-based positioning techniques have been proposed which use measurements obtained within the cellular networks, such as received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) methods [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Location estimation using RSS measurements with unknown path loss exponents

Zeytinci

Sari

Harmanci

et al. 2013

J Wireless Com Network

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The location of a mobile station (MS) in a cellular network can be estimated using received signal strength (RSS) measurements that are available from control channels of nearby base stations. Most of the recent RSS-based location estimation methods that are available in the literature rely on the rather unrealistic assumption that signal propagation characteristics are known and independent of time variations and the environment. In this paper, we propose an RSS-based location estimation technique, so-called multiple path loss exponent algorithm (RSS-MPLE), which jointly estimates the propagation parameters and the MS position. The RSS-MPLE method incorporates antenna radiation pattern information into the signal model and determines the maximum likelihood estimate of unknown parameters by employing the Levenberg-Marquardt method. The accuracy of the proposed method is further examined by deriving the Cramer-Rao bound. The performance of the RSS-MPLE algorithm is evaluated for various scenarios via simulation results which confirm that the proposed scheme provides a practical position estimator that is not only accurate but also robust against the variations in the signal propagation characteristics.

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“…Pattern matching localization method is proposed to overcome the limitations of traditional methods (Bahl, 2000), (Laitinen, 2001), (Ahonen, 2003), (Borkowski, 2005). Under the pattern matching method, a user terminal measures the radio signal pattern, and then, seeks for the most similar pattern in the pattern database, which consists of the radio signal patterns gathered at the specified positions a priori.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, the position of the user terminal is estimated. (Bahl, 2000) proposes a pattern matching method for wireless local area networks, while (Laitinen, 2001), (Ahonen, 2003), (Borkowski, 2005) employ the pattern matching in cellular networks.…”

Section: Related Workmentioning

confidence: 99%

Use of AGPS Call Data Records for Non-GPS Terminal Positioning in Cellular Networks

Lee

Kwon

et al.

E-Business and Telecommunication Networks

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Abstract:This paper presents a novel method of user terminal positioning in cellular networks. A pattern matching technology based on received-signal-strength (RSS) from the pilot channels of cell towers has been the most popular network-based positioning method. In response to a position request, a terminal measures RSSs of pilot channels from surrounding cell towers and then the terminal's RSS pattern is compared with a pattern database to find the most correlated one which indicates the position of the terminal. Although the pattern matching method can provide accurate positioning, its database construction and maintenance require a high overhead of periodic labor-intensive pattern collection. In this paper, we propose to exploit the call data records (CDRs) that are uploaded by Assisted Global Positioning System (AGPS) terminals as inputs to the pattern database, which removes or reduces the pattern collection overhead. In AGPS systems, terminals measure satellite signals and cellular network parameters (such as RSS) and relay them to the cellular infrastructure, which in turn calculates the terminal position using both that satellite and cellular network data. The proposed AGPS CDR based pattern matching method takes advantage of the increasing number of AGPS terminals in service: non-AGPS terminals can obtain more precise positioning results in areas where more AGPS calls are generated (e.g. hotspots).To do so, we analyze the characteristics of RSS patterns and AGPS CDRs. Based on the analysis, a pattern-distance metric and an AGPS CDR based pattern matching system are proposed and their performances are evaluated by examining field data of several urban downtown areas of Seoul, Korea. We obtain promising results: the position of the user terminal can be estimated with the accuracy (or, positioning error) at the level of 96.5m and 149.8m for the 67% and the 95% confidence interval, respectively.

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Database correlation method for UMTS location

Cited by 35 publications

References 5 publications

Nonparametric Bayesian Filtering for Location Estimation, Position Tracking, and Global Localization of Mobile Terminals in Outdoor Wireless Environments

Nonparametric Bayesian Filtering for Location Estimation, Position Tracking, and Global Localization of Mobile Terminals in Outdoor Wireless Environments

Location estimation using RSS measurements with unknown path loss exponents

Use of AGPS Call Data Records for Non-GPS Terminal Positioning in Cellular Networks

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