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Position Estimation in Uncertain Radio Environments and Trajectory Learning
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
2017 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2017. , s. 45
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1772
Nationell ämneskategori
Reglerteknik Signalbehandling Sannolikhetsteori och statistik Datorseende och robotik (autonoma system) Beräkningsmatematik
Identifikatorer
URN: urn:nbn:se:liu:diva-135425DOI: 10.3384/lic.diva-135425ISBN: 9789176855591 (tryckt)OAI: oai:DiVA.org:liu-135425DiVA, id: diva2:1081592
Presentation
2017-03-29, Visionen, Hus B, Campus Valla, Linköping, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2017-03-14 Skapad: 2017-03-14 Senast uppdaterad: 2019-10-12Bibliografiskt granskad
Delarbeten
1. Received-Signal-Strength Threshold Optimization Using Gaussian Processes
Öppna denna publikation i ny flik eller fönster >>Received-Signal-Strength Threshold Optimization Using Gaussian Processes
2017 (Engelska)Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, nr 8, s. 2164-2177Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2017
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:liu:diva-135065 (URN)10.1109/TSP.2017.2655480 (DOI)000395827100018 ()
Projekt
TRAX
Anmärkning

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

Tillgänglig från: 2017-03-08 Skapad: 2017-03-08 Senast uppdaterad: 2019-02-12Bibliografiskt granskad
2. Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
Öppna denna publikation i ny flik eller fönster >>Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
Visa övriga...
2016 (Engelska)Ingår i: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), IEEE , 2016, s. 1-5Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

Ort, förlag, år, upplaga, sidor
IEEE, 2016
Nationell ämneskategori
Kommunikationssystem
Identifikatorer
urn:nbn:se:liu:diva-128255 (URN)10.1109/VTCSpring.2016.7504255 (DOI)000386528400206 ()9781509016983 (ISBN)
Konferens
2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China
Tillgänglig från: 2016-05-24 Skapad: 2016-05-24 Senast uppdaterad: 2019-02-12Bibliografiskt granskad
3. Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data
Öppna denna publikation i ny flik eller fönster >>Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data
Visa övriga...
2016 (Engelska)Ingår i: 19th International Conference on  Information Fusion (FUSION), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1461-1468Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernelbased methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to other use cases with device trajectories of positioned data. Some specific aspects can be addressed when the data is periodic, like in sports where the event is split up over multiple laps along a specific track. Flow models of both the individual skier and a cluster of skiers are derived and analyzed. Performance has been evaluated using data from the Falun Nordic World Ski Championships 2015, in particular the Men’s cross country 4 × 10 km relay. The results show that the flow models vary spatially for different skiers and clusters. We further demonstrate that GP regression provides powerful and accurate models for flow prediction.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2016
Nationell ämneskategori
Elektroteknik och elektronik Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:liu:diva-129758 (URN)9780996452748 (ISBN)9781509020126 (ISBN)
Konferens
19th International Conference on Information Fusion, 5-8 July 2016, Heidelberg, Germany
Tillgänglig från: 2016-06-27 Skapad: 2016-06-27 Senast uppdaterad: 2019-02-12Bibliografiskt granskad

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