A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

Nurminen, Henri; Dashti, Marzieh; Pich, Robert

doi:10.1155/2017/2569645

Cited by 30 publications

(24 citation statements)

References 34 publications

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“…The finding of the study proved that discarding these erroneous UE measurements increased the effectiveness of the small cell localization algorithm where large location errors (larger than 100 meters) can be eliminated compared to non RF fingerprint methods [32].The study claims to provide an accuracy of 20-50% improvement of the serving cell size where a typical cell radius of 250m yields location accuracy of approximately 50m to 125m. The result is consistent with other studies where outliers proved to be the concentration of other studies regardless of the algorithm used [2,10,14,18,24,32,33]. Li [14] approach of handling outliers was to characterize the crowdsourced datasets into spatial, localization algorithm and combined RSS with spatial.…”

Section: Outlierssupporting

confidence: 87%

“…Estimated Basestation Position 1 Centroid [15] Middle of geometric measurement 2 Minimum Enclosing Circle [16] Middle of minimum enclosing circle of total samples 3 Weighted Centroid [17] Middle of RSS-weighted geometric samples 4 Grid based [18] Middle of grid within likelihood of strongest RSS 5 Ecolocation [19] Position with the highest statistical value for RSS-distance relationship heatmap 6 Highest RSS [20] Position of sample with the highest RSS value 7 Model based [21][22][23] Position of the strongest predicted RSS using a model tuned propagation model both XML and CDR methods require permission from the MNO and adherence to privacy laws. In summary, public databases are a good source of data as it contains a large set of data from crowdsourcing which offer researchers volume across different terrain, countries, and technology.…”

Section: # Localization Approachmentioning

confidence: 99%

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A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

2019

IJRTE

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The vast amount of mobile smartphone users provides an infinite source of data for crowdsourcing. Crowdsourcing provides an economical method of gathering data to cover a large geographical area compared to traditional methods. However, the inaccurate predictions for base station localization derived from mobile crowdsourcing impacts its effectiveness for use in radio planning. Therefore, the purpose of this study is to design a model that can yield a more accurate localization through the introduction of a rule-based weighted classification. The methodology deployed is a permutation series based on fingerprint of the cell site with weightage derived from rule-based classification. DeLaunay triangulation and Voronoi diagrams are used to determine the positions of the existing base stations and the prediction of new site location respectively. The expected results are better accuracy of the classification model in the localization prediction of the base station leading to a more accurate prediction of new site location.

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Section: Outlierssupporting

confidence: 87%

Section: # Localization Approachmentioning

confidence: 99%

A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

2019

IJRTE

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“…We estimate the location of the transmitter from the measurements of RSS at the reporting sensors by the weighted centroid approach [31]. Rather than using just the current RSS measurements, z rts , we propose to include all the past information about the transmitter location as…”

Section: A Estimation Of the Transmitter Locationmentioning

confidence: 99%

“…This also allows the GP to learn more quickly and to correct for the modeling errors. In other words, GPs, being quite robust, will compensate for errors in the estimation of the mean term, i.e., in the exponent loss and transmitted power in (31) and (34).…”

Section: A the Kernel And Its Hyper-parametersmentioning

confidence: 99%

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Santos

Murillo-Fuentes

Djurić

2019

IEEE Trans. Signal Process.

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In this paper, we address the estimation of a timevarying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method. We consider the locations of the sensors to be imperfectly known, which entails that they represent another source of error in the model. The propagation model includes shadowing which is considered to be a zero-mean Gaussian process where the correlation of attenuation between two spatial points is quantified by an exponential function of the distance between the points. The location of the transmitter is also unknown and is estimated from the data. We propose to estimate time-varying RSS fields by a recursive Bayesian method and crowdsourcing. The method is based on Gaussian processes (GP), and it produces the joint distribution of the spatial field. Further, it summarizes all the acquired information by keeping the size of the needed memory bounded. We also present the Bayesian Cramér-Rao bound (BCRB) of the estimated parameters. Finally, we illustrate the performance of our method with experimental results on synthetic and real data sets.

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“…In range-based techniques, time difference of arrival (TDoA), time of arrival (ToA), angle of arrival (AoA), and received signal strength indicator (RSSI) are dominant [1][2][3][4][5][6][7]. TDoA and ToA are sensitive to timing errors; thus, accurate time synchronization between the readers (receivers) of these IoT devices (senders) is crucial.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Alma'aitah

Alsaify

Bani-Hani

2019

Sensors

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Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of 10 m × 10 m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

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A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

Cited by 30 publications

References 34 publications

A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

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