Data fusion of power and time measurements for mobile terminal location

McGuire, Michael; Plataniotis, Konstantinos N.; Venetsanopoulos, A.N.

doi:10.1109/tmc.2005.24

Cited by 93 publications

(62 citation statements)

References 35 publications

(48 reference statements)

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“…McGuire et al explored data fusion of RSS and TOA measurements for mobile terminal localization in a CDMA cellular network [69]. Li et al considered mobile user localization using hybrid TDOA/AOA measurements in a macrocell wideband CDMA system with frequency division duplex [70].…”

Section: F Localization Based On Hybrid Measurementsmentioning

confidence: 99%

Wireless sensor network localization techniques

Mao¹,

Fi̇dan²,

Anderson³

2007

Computer Networks

1,613

924

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Abstract-Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multihop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them.

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Section: F Localization Based On Hybrid Measurementsmentioning

confidence: 99%

Wireless sensor network localization techniques

Mao¹,

Fi̇dan²,

Anderson³

2007

Computer Networks

1,613

924

View full text Add to dashboard Cite

show abstract

“…In particular, the received signal strength (RSS) at a number of receivers (or sensors) permits passive localization of an emitter/target (or transmitter) whose transmission power is known 1 . As such, RSS-based localization has been considered as a promising technology for providing location information in sensor networks [4], mobile/wireless computing [5] and cellular phone networks [6] etc.…”

Section: Introductionmentioning

confidence: 99%

Optimality Analysis of Sensor-Target Geometries in Passive Localization: Part 1 - Bearing-Only Localization

Bishop

Fi̇dan

Anderson

et al. 2007

2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information

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Abstract-In this paper we characterize the bounds on localization accuracy in signal strength based localization. In particular, we provide a novel and rigorous analysis of the relative receiver-transmitter geometry and the effect of this geometry on the potential localization performance. We show that uniformly spacing sensors around the target is not optimal if the sensortarget ranges are not identical and is not necessary in any case. Indeed, we show that in general the optimal sensor-target geometry for signal strength based localization is not unique.

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“…We reformulate the fine localization problem into two optimization algorithms. One, known as Least Absolute Shrinkage and Selection Operator (LASSO) [68], seeks for a sparse position vector which satisfies two criteria: (1) The position vector is sparse so that only the most similar RPs give weight to the estimated pose; (2) The difference between the radio map and online measurements is minimized as the sparsity is not sufficient and the selected RPs' fingerprints should be the most similar to the online measurement. Also, since the resultant modified radio map may contain fingerprints in different orientations from a reference point, it is prone to have similar, partially correlated entries.…”

Section: Proposed Localization System Frameworkmentioning

confidence: 99%

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

Khalajmehrabadi

Gatsis

Pack

et al. 2017

IEEE Trans. on Mobile Comput.

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Abstract-In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

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Data fusion of power and time measurements for mobile terminal location

Cited by 93 publications

References 35 publications

Wireless sensor network localization techniques

Wireless sensor network localization techniques

Optimality Analysis of Sensor-Target Geometries in Passive Localization: Part 1 - Bearing-Only Localization

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

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