A new approach to the geometry of TOA location

Caffery, J.

doi:10.1109/vetecf.2000.886153

Cited by 372 publications

(259 citation statements)

References 7 publications

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“…Direct solutions may be used when TOA measurement errors are not considered. The source position can be directly calculated from different sets of geometric equations (Schmidt, 1972;Fang, 1990;and Caffery, 2000). The computational complexity of this approach to source localization is low, but frequently does not result in a source location solution when TOA measurement errors exist.…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…Furthermore, when additional LOPs are available in an ODS, the source position needs to be estimated by averaging all the possible source locations (LOP intersections). A linear least-squares (LS) approach is an alternative source location estimation technique (Caffery, 2000) that can provide a global solution using simple computational steps for situations when only the minimum number of sensors (MRS) is available. It can also be used in the ODS case.…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…has been a subject of research for decades and continues to receive much interest in the signal processing research community, including radar (Bahl andPadmanabhan, 2000 andYan et al, 2007), sonar (Carter, 1981;Leonard and Durrant-Whyte, 1991;and Leonard and Durrant-Whyte, 2012), mobile communications (Caffery, 2000;Gustafsson and Gunnarsson, 2005;and Caffery and Stuber, 1998), multimedia (Brandstein and Silverman, 1997;Wang and Chu, 1997;and Akyildiz et al, 2007), animal tracking (Spiesberger and Fristrup, 1990), wireless sensor networks (WSNs; Akyildiz et al, 2002;Chen et al, 2002;Patwari et al, 2005;and Mao et al, 2007), and the Global Positioning System (GPS; Hofmann-Wellenhof et al, 2013). However, uncertainties resulting from the modulation of a signal propagated through an inhomogeneous medium and in measurements of received signals can quickly degrade position estimation accuracy when using standard methods to estimate transmitter position (Sayed et al, 2005;Güvenç and Chong, 2009;and Tan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

119

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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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Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

119

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“…Localization by time-of-arrival (TOA) [48], [49], [50] measures the time a signal takes to arrive at some number of sensors. This requires knowing the time the signal was transmitted, and assumes tight time synchronization between sender and receiver.…”

Section: Measurement Phasementioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

253

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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“…It is noteworthy that this idea is first introduced in [7]- [8] for time-difference-of-arrival (TDOA) based localization. The linear equations can then be solved straightforwardly by using least squares and the corresponding estimator is referred to as the least squares calibration (LSC) method [9], or by eliminating the common variable via subtraction of each equation from all others, which is referred to as the linear least squares (LLS) estimator [10]- [11]. In this work, we will focus on relationship development between the the best linear unbiased estimator (BLUE) [12] versions of the LSC and LLS algorithms.…”

Section: Introductionmentioning

confidence: 99%

Best linear unbiased estimator approach for time-of-arrival based localisation

Chan

Zheng

et al. 2008

IET Signal Process.

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A common technique for source localization is to utilize the time-of-arrival (TOA) measurements between the source and several spatially separated sensors. The TOA information defines a set of circular equations from which the source position can be calculated with the knowledge of the sensor positions. Apart from nonlinear optimization, least squares calibration (LSC) and linear least squares (LLS) are two computationally simple positioning alternatives which reorganize the circular equations into a unique and non-unique set of linear equations, respectively. As the LSC and LLS algorithms employ standard least squares (LS), an obvious improvement is to utilize weighted LS estimation. In this paper, it is proved that the best linear unbiased estimator (BLUE) version of the LLS algorithm will give identical estimation performance as long as the linear equations correspond to the independent set. The equivalence of the BLUE-LLS approach and the BLUE variant of the LSC method is analyzed. Simulation results are also included to show the comparative performance of the BLUE-LSC, BLUE-LLS, LSC, LLS and constrained weighted LSC methods with Cramér-Rao lower bound.

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A new approach to the geometry of TOA location

Cited by 372 publications

References 7 publications

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

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

A Survey on Localization for Mobile Wireless Sensor Networks

Best linear unbiased estimator approach for time-of-arrival based localisation

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