Localization of objects using stochastic tunneling

Basheer, Mohammed Rana; Jagannathan, S.

doi:10.1109/wcnc.2011.5779199

Cited by 3 publications

(13 citation statements)

References 20 publications

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“…The linearity between correlation measurements and radial distance is restricted in these approaches to a small area containing K nearest neighbors. However, the linearity between RSS and radial distance does not hold in Li and Liu [19], even in the immediate vicinity of operating frequencies greater than 10 MHz.…”

Section: Related Workmentioning

confidence: 93%

“…The multi-dimensional scaling (MDS) model has been used to reduce dimensionality to estimate locations [16]. However, the linear relationship requirement between correlation coefficients and radial distance in MDS has restricted its applications to wireless environments where RSS correlations are highly nonlinear if there is a radial distance [19] between receivers. Manifold learning (reduced nonlinear dimensionality) algorithms such as Isomap, Local Linear Embedding (LLE) and Hessian LLE have been used to centralize localization [17], [18].…”

Section: Related Workmentioning

confidence: 99%

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Mobile Receiver-Assisted Localization Based on Selective Coordinates in Approach to Estimating Proximity for Wireless Sensor Networks

Hussin¹,

Nakamoto²

2014

IJACSA

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Abstract-Received signal strength (RSS)-based mobile localization has become popular due to its inexpensive localization solutions in large areas. Compared to various physical properties of radio signals, RSS is an attractive approach to localization because it can easily be obtained through existing wireless devices without any additional hardware. Although RSS is not considered to be a good choice for estimating physical distances, it provides some useful distance related information in adding and indicating connectivity information in neighboring nodes. RSS-based localization is generally divided into range-based and rangefree. Range-based localization can achieve excellent accuracy but is too costly to apply to large-scale networks. Methods of range-free localization are regarded as cost-effective solutions for localization in sensor networks. However, the localizations are subject to the effect of radio patterns that affect variations in the radial distance estimates between nodes. It is a challenging task to select an efficient RSS value that can provide small variations in the radial distance in wireless environments. We propose a method of Mobile Localization using the Proximities of Selective coordinates (MoLPS) to localize target nodes by using information on proximities between target nodes and mobile receivers as a metric to estimate the location of target nodes. We ran a simulation experiment to assess the performance of MoLPS with 100 target nodes that were randomly deployed along a sensory field boundary. We found from the results of the simulation experiment that localization error had been reduced to below 2m in more than 80% of the target nodes.

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Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Mobile Receiver-Assisted Localization Based on Selective Coordinates in Approach to Estimating Proximity for Wireless Sensor Networks

Hussin¹,

Nakamoto²

2014

IJACSA

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“…Transmitter localization from correlated noise measurements observed at adjacent receivers was investigated in [5]. However, the method relied on correlation between multipath fading noise which, as pointed out in [5], falls rapidly to zero within one wavelength( ) of radial separation between receiver and transmitter thereby relegating this technique for localization solutions that operate at frequencies 100 .…”

Section: Introductionmentioning

confidence: 99%

“…However, the method relied on correlation between multipath fading noise which, as pointed out in [5], falls rapidly to zero within one wavelength( ) of radial separation between receiver and transmitter thereby relegating this technique for localization solutions that operate at frequencies 100 . In [6] localization was addressed as a dimensionality reduction problem using Multi-Dimensional Scaling (MDS).…”

Section: Introductionmentioning

confidence: 99%

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Localization of Objects Using Cross-Correlation of Shadow Fading Noise and Copulas

Basheer

Jagannathan

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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While fading, in general, is detrimental to accurately localizing a target, fading correlation between adjacent receivers may be exploited to improve localization accuracy. Therefore, this paper presents a novel wireless localization scheme that employs a combination of cross-correlation between shadow fading noise and copula technique to recursively estimate the location of a transmitter. A stochastic filter that models multipath fading as an Ornstein-Uhlenbeck process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering is proposed to extract shadow fading residuals from measured RSSI values. Subsequently, Student-t Copula function is used to create the log likelihood function, which acts as the cost function for localization, by combining spatial shadow fading correlation arising among adjacent receivers due to pedestrian traffic in the area. Maximum Likelihood Estimate (MLE) is used for position estimation as it inherits the statistical consistency and asymptotic normality. The performance of our proposed localization method is validated over simulations and hardware experiments.

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Localization and Tracking of Objects Using Cross-Correlation of Shadow Fading Noise

Basheer

Jagannathan

2014

IEEE Trans. on Mobile Comput.

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Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless transmitter localization scheme that combines the measured cross-correlation in shadow fading residuals between adjacent receivers using a Student-t Copula likelihood function is proposed. However, the long convergence time for this highly non-convex copula function might render our method unsuitable for tracking applications. Therefore, we present a faster tracking method where the velocity and heading of a mobile transmitter are estimated from α-Divergence between shadow fading signals and an onboard gyroscope respectively. To bind the localization error in this tracking method, the transmitter location estimates are smoothed by a Bayesian particle filter. The performance of our proposed localization and tracking method is validated over simulations and hardware experiments.

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Localization of objects using stochastic tunneling

Cited by 3 publications

References 20 publications

Mobile Receiver-Assisted Localization Based on Selective Coordinates in Approach to Estimating Proximity for Wireless Sensor Networks

Mobile Receiver-Assisted Localization Based on Selective Coordinates in Approach to Estimating Proximity for Wireless Sensor Networks

Localization of Objects Using Cross-Correlation of Shadow Fading Noise and Copulas

Localization and Tracking of Objects Using Cross-Correlation of Shadow Fading Noise

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