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Time of Flight Estimation for Radio Network Positioning
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. (Automatic Control)ORCID iD: 0000-0001-9913-3652
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Trilateration is the mathematical theory of computing the intersection of circles. These circles may be obtained by time of flight (ToF) measurements in radio systems, as well as laser, radar and sonar systems. A first purpose of this thesis is to survey recent efforts in the area and their potential for localization. The rest of the thesis then concerns selected problems in new cellular radio standards as well as fundamental challenges caused by propagation delays in the ToF measurements, which cannot travel faster than the speed of light. We denote the measurement uncertainty stemming from propagation delays for positive noise, and develop a general theory with optimal estimators for selected distributions, which can be applied to trilateration but also a much wider class of estimation problems.

The first contribution concerns a narrow-band mode in the long-term evolution (LTE) standard intended for internet of things (IoT) devices. This LTE standard includes a special position reference signal sent synchronized by all base stations (BS) to all IoT devices. Each device can then compute several pair-wise time differences that correspond to hyperbolic functions. The simulation-based performance evaluation indicates that decent position accuracy can be achieved despite the narrow bandwidth of the channel.

The second contribution is a study of how timing measurements in LTE can be combined. Round trip time (RTT) to the serving BS and time difference of arrival (TDOA) to the neighboring BS are used as measurements. We propose a filtering framework to deal with the existing uncertainty in the solution and evaluate with both simulated and experimental test data. The results indicate that the position accuracy is better than 40 meters 95% of the time.

The third contribution is a comprehensive theory of how to estimate the signal observed in positive noise, that is, random variables with positive support. It is well known from the literature that order statistics give one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations and summaries of estimators based on order statistics, including the BLUE one for comparison. An iterative global navigation satellite system (GNSS) localization algorithm, based on the derived estimators, is introduced to jointly estimate the receiver’s position and clock bias.

The fourth contribution is an extension of the third contribution to a particular approach to utilize positive noise in nonlinear models. That is, order statistics have been employed to derive estimators for a generic nonlinear model with positive noise. The proposed method further enables the estimation of the hyperparameters of the underlying noise distribution. The performance of the proposed estimator is then compared with the maximum likelihood estimator when the underlying noise follows either a uniform or exponential distribution.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. , p. 78
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2054
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-163589DOI: 10.3384/diss.diva-163589ISBN: 9789179298845 (print)OAI: oai:DiVA.org:liu-163589DiVA, id: diva2:1393383
Public defence
2020-03-20, Ada Lovelace, House B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

Ytterligare forskningsfinansiär: European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems(TRAX): Grant number 607400

Available from: 2020-02-17 Created: 2020-02-16 Last updated: 2020-02-19Bibliographically approved
List of papers
1. Performance of OTDOA Positioning in Narrowband IoT Systems
Open this publication in new window or tab >>Performance of OTDOA Positioning in Narrowband IoT Systems
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2017 (English)In: 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): Proceedings, IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Narrowband Internet of Things (NB-IoT) is an emerging cellular technology designed to target low-cost devices, high coverage, long device battery life (more than ten years), and massive capacity. We investigate opportunities for device tracking in NB-IoT systems using Observed Time Difference of Arrival (OTDOA) measurements. Reference Signal Time Difference (RSTD) reports are simulated to be sent to the mobile location center periodically or on an ondemand basis. We investigate the possibility of optimizing the number of reports per minute budget on horizontal positioning accuracy using an on-demand reporting method based on the Signal to Noise Ratio (SNR) of the measured cells received by the User Equipment (UE). Wireless channels are modeled considering multipath fading propagation conditions. Extended Pedestrian A (EPA) and Extended Typical Urban (ETU) delay profiles corresponding to low and high delay spread environments, respectively, are simulated for this purpose. To increase the robustness of the filtering method, measurement noise outliers are detected using confidence bounds estimated from filter innovations.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Communication Systems Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-142819 (URN)10.1109/PIMRC.2017.8292365 (DOI)000426970901053 ()978-1-5386-3531-5 (ISBN)978-1-5386-3529-2 (ISBN)
Conference
28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, 08-13 October, 2017
Projects
Tracking in complex sensor systems, TRAX
Available from: 2017-11-06 Created: 2017-11-06 Last updated: 2020-02-16
2. Fusion of TOF and TDOA for 3GPP Positioning
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: 2020-02-16Bibliographically approved

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