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Received-Signal-Strength Threshold Optimization Using Gaussian Processes
Chinese University of Hong Kong, China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. (Automatic Control)ORCID iD: 0000-0003-1214-2391
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Ericsson Research .
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2017 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 8, p. 2164-2177Article in journal (Refereed) Published
Abstract [en]

There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic received-signal-strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and management. Among others, we focus on Gaussian process regression (GPR)-based RSS models and positioning metric computation. The optimal RSS threshold is found through minimizing the best achievable localization root-mean-square-error formulated with the aid of fundamental lower bound analysis. Computational complexity is compared for different RSS models and different fundamental lower bounds. The resulting optimal RSS threshold enables enhanced performance of new fashioned low-cost and low-complex proximity report-based positioning algorithms. The proposed framework is validated with real measurements collected in an office area where bluetooth-low-energy (BLE) beacons are deployed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 65, no 8, p. 2164-2177
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-135065DOI: 10.1109/TSP.2017.2655480ISI: 000395827100018OAI: oai:DiVA.org:liu-135065DiVA, id: diva2:1079298
Projects
TRAX
Note

Funding agencies: European Union FP7 Marie Curie training programme on Tracking in Complex Sensor Systems [607400]

Available from: 2017-03-08 Created: 2017-03-08 Last updated: 2018-03-27Bibliographically approved
In thesis
1. Position Estimation in Uncertain Radio Environments and Trajectory Learning
Open this publication in new window or tab >>Position Estimation in Uncertain Radio Environments and Trajectory Learning
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics.

The main contribution of this thesis is to propose sequential Monte Carlo methods, namely particle filtering and smoothing, for a novel indoor positioning framework based on proximity reports. The experiment results have been further compared with theoretical bounds derived for this proximity based positioning system. To improve the performance, Bayesian non-parametric modeling, namely Gaussian process, has been applied to better indicate the radio propagation conditions. Then, the position estimates obtained sequentially using filtering and smoothing are further compared with a static solution, which is known as fingerprinting.

Moreover, we propose a trajectory learning framework for flow estimation in sport analytics based on Gaussian processes. To mitigate the computation deficiency of Gaussian process, a grid-based on-line algorithm has been adopted for real-time applications. The resulting trajectory modeling for individual athlete can be used for many purposes, such as performance prediction and analysis, health condition monitoring, etc. Furthermore, we aim at modeling the flow of groups of athletes, which could be potentially used for flow pattern recognition, strategy planning, etc.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. p. 45
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1772
National Category
Control Engineering Signal Processing Probability Theory and Statistics Computer Vision and Robotics (Autonomous Systems) Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-135425 (URN)10.3384/lic.diva-135425 (DOI)9789176855591 (ISBN)
Presentation
2017-03-29, Visionen, Hus B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2017-03-14 Created: 2017-03-14 Last updated: 2018-01-13Bibliographically approved

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