Decision theoretic framework for NLOS identification

Borras, Jordi; Hatrack, P.; Mandayam, Narayan B.

doi:10.1109/vetec.1998.686556

Cited by 227 publications

(166 citation statements)

References 7 publications

Supporting

Mentioning

161

Contrasting

Unclassified

Order By: Relevance

“…In matrix notation, the output of the virtual array is given as (13) where is the matrix of the array responses of all sensors for all DOA"s. The correlator cross-correlates the array signal with that of the output of the virtual array as (14) where R is K L matrix. The spatial spectrum of the SDMA receiver is then (15) where (16) The peaks of P SDMA correspond to the DOAs of the incident signals.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

Niaz¹,

Khan²,

Shafi³

2011

IJCA

View full text Add to dashboard Cite

This paper proposes a source localization scheme using random arrays of Wireless Sensor Networks (WSN). A Total Least Square (TLS) estimator is proposed which improves the result of the location of source node. Using a relatively new Direction of Arrival (DOA) estimation technique Space Division Multiple Access (SDMA) receiver the proposed solution is able to perform localization in a multipath environment. The propose scheme considers both Line of Sight (LOS) and Non Line of Sight (NLOS) signals to perform the localization with the TLS estimator which is efficient than a simple Least Square (LS) estimator. Simulation results are included to demonstrate that the proposed solution provides an improved estimate by exploiting the NLOS information, SDMA receiver and using TLS estimator.

show abstract

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

Niaz¹,

Khan²,

Shafi³

2011

IJCA

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

View full text Add to dashboard Cite

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.

show abstract

“…Mobile locating and tracking is actually the problem of location prediction, and NLOS is one of the most factors that bring errors [6]. Even we can apply some techniques to alleviate this kind of predicting error; the predicted location still deviates from the real trajectory of the target.…”

Section: Introductionmentioning

confidence: 99%

Game Theory Based Location Smoothing in Wireless Sensor Networks

Yongqiang

Qin²,

Wang³

2016

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Abstract-Location estimation of mobile target is an important research in wireless sensor networks. However, the phenomenon of non line of sight affects the accuracy of location estimating algorithms greatly. In order to improve the accuracy of location estimation of mobile targets, we proposed a game theory based location method algorithm in wireless sensor networks. In the proposed method, we first estimate the initial locations of mobile target with a support vector regression model, and then use game theory to smooth the already estimated locations. In the game model, game based filter and noise generator are two game rivals. The game filter aims to minimize the objective function of the game model, and the noise generator aims to maximize the same objective function. We propose a differential game theory based algorithm for solving the optimal resolution. The experiments show that, the proposed method is more powerful, and can be used to mobile target locating and tracking.

show abstract

Decision theoretic framework for NLOS identification

Cited by 227 publications

References 7 publications

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

An Underwater Robotic Network for Monitoring Nuclear Waste Storage Pools

Game Theory Based Location Smoothing in Wireless Sensor Networks

Contact Info

Product

Resources

About