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Gunnarsson, Fredrik
Alternative names
Publications (10 of 123) Show all publications
Yin, F., Zhao, Y. & Gunnarsson, F. (2016). Fundamental Bounds on Position Estimation Using Proximity Reports. In: : . Paper presented at IEEE 83rd Vehicular Technology Conference: VTC2016-Spring 15–18 May 2016, Nanjing, China.
Open this publication in new window or tab >>Fundamental Bounds on Position Estimation Using Proximity Reports
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

There is a big trend nowadays toward indoor proximity report based positioning. A binary valued proximity report can be obtained opportunistically through event-triggering, leading to significantly reduced signaling overhead for wireless communications. In this paper, we aim to derive two types of fundamental lower bound, namely the Cram´er-Rao bound and the Barankin bound, on the mean-square-error of any proximity report based position estimator. Using the maximum-likelihood estimator as a representative example, we show that the Barankin bound is potentially much tighter than the Cram´er-Rao bound and conclude that the Barankin bound ought be better suited for benchmarking any proximity report based position estimator.

Keyword
Barankin bound, Cramér-Rao bound, mean-square-error, position estimation, proximity report.
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-129760 (URN)
Conference
IEEE 83rd Vehicular Technology Conference: VTC2016-Spring 15–18 May 2016, Nanjing, China
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2016-07-06Bibliographically approved
Radnosrati, K., Fritsche, C., Hendeby, G., Gunnarsson, F. & Gustafsson, F. (2016). Fusion of TOF and TDOA for 3GPP Positioning. In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings. Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016 (pp. 1454-1460). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Fusion of TOF and TDOA for 3GPP Positioning
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2016 (English)In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1454-1460Conference paper, Published paper (Refereed)
Abstract [en]

Positioning in cellular networks is often based on mobile-assisted measurements of serving and neighboring base stations. Traditionally, positioning is considered to be enabled when the mobile provides measurements of three different base stations. In this paper, we additionally investigate positioning based on time series of Time Of Flight (TOF) and Time Difference of Arrival (TDOA) measurements gathered from two base stations with known positions, where the specific base stations involved depend on the trajectory of the mobile station.. The set of two base stations is different along the trajectory. Each report contains TOF for the serving base station, and one TDOA measurement for the most favorable neighboring base station relative the serving base station. We derive explicit analytical solution related to the intersection of the absolute distance circle (from TOF) and relative distance hyperbola (from TDOA). We consider both geometric noise-free problem and the more realistic problem with additive noise as delivered in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE). Positioning performance is evaluated using the Cramer-Rao lower bound.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130209 (URN)000391273400193 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Projects
TRAX
Funder
EU, FP7, Seventh Framework Programme, 607400
Available from: 2016-07-15 Created: 2016-07-15 Last updated: 2017-02-03Bibliographically approved
Zhao, Y., Yin, F., Gunnarsson, F., Amirijoo, M. & Hendeby, G. (2016). Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning. In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring): . Paper presented at 2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China (pp. 1-5). IEEE
Open this publication in new window or tab >>Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
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2016 (English)In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), IEEE , 2016, p. 1-5Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-128255 (URN)10.1109/VTCSpring.2016.7504255 (DOI)000386528400206 ()9781509016983 (ISBN)
Conference
2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China
Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2017-09-13Bibliographically approved
Zhao, Y., Yin, F., Gunnarsson, F., Hultkratz, F. & Fagerlind, J. (2016). Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data. In: 19th International Conference on  Information Fusion (FUSION), 2016: . Paper presented at 19th International Conference on Information Fusion, 5-8 July 2016, Heidelberg, Germany (pp. 1461-1468). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data
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2016 (English)In: 19th International Conference on  Information Fusion (FUSION), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1461-1468Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-129758 (URN)9780996452748 (ISBN)9781509020126 (ISBN)
Conference
19th International Conference on Information Fusion, 5-8 July 2016, Heidelberg, Germany
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2017-09-13Bibliographically approved
Kasebzadeh, P., Fritsche, C., Hendeby, G., Gunnarsson, F. & Gustafsson, F. (2016). Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning. In: Proceedings of the 19th International Conference on Information Fusion: . Paper presented at 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016 (pp. 379-385). IEEE conference proceedings
Open this publication in new window or tab >>Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning
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2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, IEEE conference proceedings, 2016, , p. 7p. 379-385Conference paper, Published paper (Refereed)
Abstract [en]

We consider personal navigation systems in devices equipped with inertial sensors and GPS, where we propose an improved Pedestrian Dead Reckoning (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or an indoor positioning system (IPS). A novel filtering approach is proposed that is able to learn internal gait parameters in the PDR algorithm, such as the step length and the step detection threshold. Our approach is based on a multi-rate Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in time intervals when the position is not available, for instance when passing from outdoor to indoor environments where IPS is not available. The effectiveness of the new approach is illustrated on several real world experiments. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 7
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-130174 (URN)000391273400052 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016
Funder
EU, FP7, Seventh Framework Programme, 607400
Available from: 2016-07-13 Created: 2016-07-13 Last updated: 2017-02-03Bibliographically approved
Radnosrati, K., Gunnarsson, F. & Gustafsson, F. (2015). New Trends in Radio Network Positioning. In: Proceedings of 18th International Conference on Information Fusion, 2015: . Paper presented at 18th International Conference on Information Fusion, Washington DC, USA, July 6-9 2015 (pp. 492-498). IEEE
Open this publication in new window or tab >>New Trends in Radio Network Positioning
2015 (English)In: Proceedings of 18th International Conference on Information Fusion, 2015, IEEE , 2015, p. 492-498Conference paper, Published paper (Refereed)
Abstract [en]

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth.

Place, publisher, year, edition, pages
IEEE, 2015
Keyword
Sesnsor fusion, Positioning
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Telecommunications
Identifiers
urn:nbn:se:liu:diva-121290 (URN)9780982443866 (ISBN)
Conference
18th International Conference on Information Fusion, Washington DC, USA, July 6-9 2015
Projects
TRAX
Funder
Translational Program in Diabetes Research, Education and Care
Available from: 2015-09-11 Created: 2015-09-11 Last updated: 2015-09-30Bibliographically approved
Zhao, Y., Yin, F., Gunnarsson, F., Amirijoo, M., Özkan, E. & Gustafsson, F. (2015). Particle Filtering for Positioning Based on Proximity Reports. In: : . Paper presented at 18th International Conference on Information Fusion, 2015 (pp. 1046-1052).
Open this publication in new window or tab >>Particle Filtering for Positioning Based on Proximity Reports
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2015 (English)Conference paper, Published paper (Refereed)
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 positioning of devices based on time series proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Therefore, particle filtering is applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report. Accuracy is concluded to vary spatially over the office floor, and in relation to the beacon deployment density.

National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-129755 (URN)
Conference
18th International Conference on Information Fusion, 2015
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2016-07-06Bibliographically approved
Yin, F., Zhao, Y. & Gunnarsson, F. (2015). Proximity Report Triggering Threshold Optimization for Network-Based Indoor Positioning. In: : . Paper presented at 18th International Conference on Information Fusion (pp. 1061-1069).
Open this publication in new window or tab >>Proximity Report Triggering Threshold Optimization for Network-Based Indoor Positioning
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Driven by the promising beaconing techniques, this paper presents a general received-signal-strength (RSS) threshold optimization procedure for proximity reports to support networkbased indoor positioning. The desired RSS threshold is found through optimizing a metric function (for instance the localization root-mean-square-error) in terms of both the deployment information and the RSS modeling in consideration of an evaluation area. The resulting RSS threshold provides a trade-off between triggering many but less informative proximity reports with a low threshold, and triggering few but very informative proximity reports with a high threshold, and thus enables enhanced performance for some low-cost and low-complex proximity based positioning algorithms. The proposed framework is also validated with real RSS measurements collected in an office area with deployed bluetooth low-energy (BLE) beacons.

National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-129759 (URN)
Conference
18th International Conference on Information Fusion
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2016-07-06Bibliographically approved
Sjanic, Z., Gunnarsson, F., Fritsche, C. & Gustafsson, F. (2014). Cellular Network Non-Line-of-Sight Reflector Localisation Based on Synthetic Aperture Radar Methods. IEEE Transactions on Antennas and Propagation, 62(4), 2284-2287
Open this publication in new window or tab >>Cellular Network Non-Line-of-Sight Reflector Localisation Based on Synthetic Aperture Radar Methods
2014 (English)In: IEEE Transactions on Antennas and Propagation, ISSN 0018-926X, E-ISSN 1558-2221, Vol. 62, no 4, p. 2284-2287Article in journal (Refereed) Published
Abstract [en]

The dependence of radio signal propagation on the environment is  well known, and both statistical and deterministic methods have been presented in the literature. Such methods are either based on randomised or actual reflectors of radio signals. In this work, we instead aim at estimating the location of the reflectors based on geo-localised radio channel impulse reponse measurements and using methods from synthetic aperture radar (SAR). Radio channel data measurements from 3GPP E-UTRAN have been used to verify the usefulness of the proposed approach. The obtained images show that  the estimated reflectors are well correlated with the aerial map of the environment. Also, which part of the trajectory contributed to different reflectors have been estimated with promising results.

Place, publisher, year, edition, pages
IEEE Press, 2014
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-97279 (URN)10.1109/TAP.2014.2300531 (DOI)000334744700057 ()
Available from: 2013-09-09 Created: 2013-09-05 Last updated: 2017-12-06
Gunnarsson, F., Lindsten, F. & Carlsson, N. (2014). Particle filtering for network-based positioning terrestrial radio networks. In: Data Fusion & Target Tracking 2014: Algorithms and Applications (DF&TT 2014), IET Conference on: . Paper presented at IET Conference on Data Fusion and Target Tracking 2014: Algorithms and Applications. Institution of Engineering and Technology, 2014(629 CP)
Open this publication in new window or tab >>Particle filtering for network-based positioning terrestrial radio networks
2014 (English)In: Data Fusion & Target Tracking 2014: Algorithms and Applications (DF&TT 2014), IET Conference on, Institution of Engineering and Technology , 2014, Vol. 2014, no 629 CPConference paper, Published paper (Refereed)
Abstract [en]

There is strong interest in positioing in wireless networks, partly to support end user service needs, but also to support network management with network-based network information. The focus in this paper is on the latter, while using measurements that are readily available in wireless networks. We show how thesignal direction of departure and inter-distance between the base station and the mobile terminal can be estimated, and how particle filters and smoothers can be used to post-process the measurements. The methods are evaluated in a live 3GPP LTE network with promising results inlcuding position error medians of less than 100 m.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2014
Series
IET Conference Publications Series
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-116743 (URN)10.1049/cp.2014.0523 (DOI)2-s2.0-84902687636 (Scopus ID)9781849198639 (ISBN)
Conference
IET Conference on Data Fusion and Target Tracking 2014: Algorithms and Applications
Note

Funding Agencies|SSF, Swedish Foundation for Strategic Research

Available from: 2015-04-09 Created: 2015-04-02 Last updated: 2015-04-20
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