The spherical interpolation method of source localization

Smith, Julius O.; Abel, Jonathan S.

doi:10.1109/joe.1987.1145217

Cited by 163 publications

(87 citation statements)

References 6 publications

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“…Location estimates have been formed by the exact solutions to the hyperbolic TDOA equations in [27,28], while other approaches have used a Taylor-series expansion to linearize the equations and create an iterative algorithm [29,30]. Several other TDOA methods are based on least squares minimization of the location error [31][32][33][34][35]. For TOA, a popular method for computing the MS location is through the method of least squares [7,15,29].…”

Section: Algorithms For Locationmentioning

confidence: 99%

Overview of radiolocation in CDMA cellular systems

1998

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WApplications for the location of subscribers of wireless services continue to expand. Consequently, location techniques for wireless technologies are being investigated. With code-division multiple access (CDMA) being deployed by a variety of cellular and PCS providers, developing an approach for location in CDMA networks is imperative. This article discusses the applications of location technology, the methods available for its implementation in CDMA networks, and the problems that are encountered when using CDMA networks for positioning.

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Section: Algorithms For Locationmentioning

confidence: 99%

Overview of radiolocation in CDMA cellular systems

1998

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“…To validate this statement, we have simulated the localization algorithms by Schmidt [24], Foy [19], Smith and Abel [20], Friedlander [21], Schau and Robinson [22], Chan and Ho [23], the application of Bancroft by Geyer and Daskalakis [25], and the Wikipedia [26]. All of these algorithms use the least-squares numerical method.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…The most representative algorithms using this kind of models are the one by Smith and Abel [20], the one by Friedlander [21], the one by Schau and Robinson [22], and the one by Chan an Ho [23]. They are a suitable representation of the most of the algorithms of this class.…”

Section: The Numerical Approach-based Modelsmentioning

confidence: 99%

“…They are a suitable representation of the most of the algorithms of this class. For example, the Smith and Abel algorithm [20] mathematically solves (7) to obtain R s as a function of θ and then uses this solution, again into (7), to obtain the corresponding solution for θ. These two solutions are obtained by minimizing the square root of ε.…”

Section: The Numerical Approach-based Modelsmentioning

confidence: 99%

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Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

Mantilla-Gaviria

Leonardi

Galati

et al. 2014

SIViP

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We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numer- ically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.

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“…To avoid iterative algorithms, two-stage, closed-form LS estimators have been extensively developed for ML approximation (Friedlander, 1987;Schau and Robinson, 1987;Smith and Abel, 1987a;Smith and Abel, 1987b;Chan and Ho, 1994;Brandstein and Silverman, 1997;Huang et al, 2001;and Cheung et al, 2004). These LS solutions can provide good initialization for iterative estimators, which converge with less computational effort to a source position estimate with higher accuracy Abel, 1987a andChan et al, 2006a).…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

117

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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The spherical interpolation method of source localization

Cited by 163 publications

References 6 publications

Overview of radiolocation in CDMA cellular systems

Overview of radiolocation in CDMA cellular systems

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

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