Time-of-Arrival Based Localization Under NLOS Conditions

Chan, Y.T.; Tsui, Wing-Yue; So, Hing-Cheung; Ching, P.C.

doi:10.1109/tvt.2005.861207

Cited by 425 publications

(209 citation statements)

References 19 publications

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“…This algorithm is similar to a weighted nonlinear LS approach when measurement errors are zero-mean Gaussian distributed (Chan et al, 2006a). Li et al (2014) improved on an algorithm published by Chan et al (2006b) using an efficient approximate ML algorithm that included coupling with bad-measurement filters for 3D source localization.…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…Güvenç and Chong (2009) presented an overview of different TOA NLOS localization algorithms with varying levels of computational complexity and prior information. Early researchers considered treatment methods that identify the NLOS sensors in an array using TOA measurements, then estimate the location of the source Insensitive to geometry, thus it is superior to Chan and Ho (1994) and Caffery (2000) Gives an exact ML estimate when three sensors on a straight line without using the NLOS sensors, or reduce the error in position estimates by weighting the NLOS less (Wylie and Holtzman, 1996;Chen, 1999;and Chan et al, 2006b). This approach is effective when a small number of the distributed sensors are NLOS, but become computationally intensive when there are many distributed sensors and may not be possible in situations where the signal source is moving.…”

Section: B Algorithms Dealing With Specific Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Section: B Algorithms Dealing With Specific Scenariosmentioning

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

View full text Add to dashboard Cite

show abstract

“…It was motivated by the requirement put forth by Federal Communications Commission (FCC) to cellular operators to be able to locate a mobile handset within an accuracy of 300 meters for 95% of calls [13]. In current literature [14,15,16,17,18,19,20,21] there are three general methods to deal with NLOS readings. The first method attempts to identify and use only LOS measurements.…”

Section: Localization In Cluttered Underwater Environmentsmentioning

confidence: 99%

“…The first method attempts to identify and use only LOS measurements. Distinguishing NLOS from LOS distance measurements could either be done using a time-varying hypothesis test [14], a probabilistic model [18] or residual information [15,21]. The second method incorporates both LOS and NLOS distance measurements with appropriate weighting to minimize the contribution of NLOS observations.…”

Section: Localization In Cluttered Underwater Environmentsmentioning

confidence: 99%

An Underwater Robotic Network for Monitoring Nuclear Waste Storage Pools

Nawaz

Hussain

Watson

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Nuclear power provides a significant portion of our current energy demand and is likely to become more wide spread with growing world population. However, the radioactive waste generated in these power plants must be stored for around 60 years in underwater storage pools before permanent disposal. These underwater storage environments must be carefully monitored and controlled to avoid an environmental catastrophe. In this paper, we present an underwater mobile sensor network that is being developed to monitor these waste storage pools. This sensing system will also be used in very old storage pools to build maps of their internal structure which can then be used for waste removal and pool decommissioning. In this paper, we outline the unique challenges of our application scenario which include robot localization in cluttered underwater environments and the effect of location errors on environment mapping. We also list other industrial applications that can benefit from our underwater sensor network.

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“…Many proposed localization methods and algorithms were based on the computation of the time of arrival (TOA) [5][6][7], time differences of arrival (TDOA) [8], direction of arrival (DOA) [9][10][11] and the received signal strength (RSS) [12,13]. Conventional methods based on these four measurements increase in error with multipath propagation because they require LoS conditions between the access points and the mobile stations.…”

Section: Introductionmentioning

confidence: 99%

The Application of Ray-Tracing to Mobile Localization Using the Direction of Arrival and Received Signal Strength in Multipath Indoor Environments

Tayebi¹,

Gómez

Adana³

et al. 2009

PIER

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Abstract-This work presents a new indoor localization method based on the fingerprinting technique. The proposed method uses a ray-tracing model that provides information about multipath effects. This information is stored in a dataset during the first stage of the fingerprinting method. The direction of arrival (DOA) and received signal strength (RSS) are used in the fingerprinting technique as a hybrid system. The localization estimation is calculated while taking into account the Euclidian distance between the DOA and the RSS from each unknown position and the information of the fingerprints. Numerical calculations were performed to show the mean and the standard deviation of the estimated error.

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Time-of-Arrival Based Localization Under NLOS Conditions

Cited by 425 publications

References 19 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

An Underwater Robotic Network for Monitoring Nuclear Waste Storage Pools

The Application of Ray-Tracing to Mobile Localization Using the Direction of Arrival and Received Signal Strength in Multipath Indoor Environments

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