Closed-form least-squares source location estimation from range-difference measurements

Smith, Julius O.; Abel, Jonathan S.

doi:10.1109/tassp.1987.1165089

Cited by 608 publications

(301 citation statements)

References 12 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 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%

“…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). Some researchers have compared the performance of algorithms (Yu and Oppermann, 2004;Shen et al, 2008;Gezici et al, 2008;and So, 2011).…”

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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“…In this classification, a localization algorithm is an entity that reconstructs the target position, given a set of TDOA measurements. The interested reader may see [7,10,11] and the references therein for some guidelines in MLAT systems, and [12] for an equivalent comparison for Global Positioning System (GPS) geolocation algorithms. However, really (as we describe later), a localization algorithm is composed of a data model, which mathematically relates the unknown and known parameters, by a system of equations, and a numerical method to solve that system of equations.…”

Section: Introductionmentioning

confidence: 99%

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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Closed-form least-squares source location estimation from range-difference measurements

Cited by 608 publications

References 12 publications

Overview of radiolocation in CDMA cellular systems

Overview of radiolocation in CDMA cellular systems

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

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

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