Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements

By Fredrik K. Gustafsson, Fredrik Gunnarsson. IEEE Signal Processing Magazine, 2005

993 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2fe363a54bd69f789e0f9cfef72efcb26acd966f

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Citations in the corpus

A Theoretical Foundation of Network Localization and Navigation (2018, neutral): “[8] F.”
A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, methodology): “In past years, various techniques based on TOA, TDOA, AOA, and signal strength have been considered for localization in cellular networks [219].”
Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks (2009, neutral): “These measurements can be time-of-arrival (TOA), time-difference-of-arrival (TDOA), received-signal-strength (RSS), and angle-of-arrival (AOA) [2].”
Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning (2009, background): “Notwithstanding recent reﬁnements in positioning algorithms, there are fundamental limits to the accuracy that can be achieved with typical densities of cell towers (Gustafsson and Gunnarsson 2005).”
Wireless sensor network localization techniques (2007, background): “Review of wireless network localization techniques can be found in [2] [3] [4].”
Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G (2018, background): “[14] 2005 Cellular Overview of the fundamental limitations of mobile positioning.”
Cooperative Localization in Wireless Networks (2009, background): “The measurement phase is affected by uncertainty due to sources such as noise, multipath, blockages, interference , clock drifts, and environmental effects [1], [19]–[24].”
Some Relations Between Extended and Unscented Kalman Filters (2012, background): “We also show analytically that the UT generally does not give the same elements in the covariance as the second-order Taylor expansion, which should at least be exact for quadratic functions of Gaussian variables.”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “Their measurements can, however, be preprocessed to obtain position estimates and their corresponding covariances [51].”
Towards 1 Gbps/UE in Cellular Systems: Understanding Ultra-Dense Small Cell Deployments (2015, background): “In order to reduce transmit power, the identification and characterisation of hot spots becomes critical [70], [71].”
Optimality analysis of sensor-target localization geometries (2010, background): “It is well known that the relative sensor-target geometry can significantly affect the potential performance of any particular localization algorithm [1,15,22].”
A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques (2009, methodology): “Minimizing the non-linear expression in (33) requires numerical search methods such as the steepest descent [31] or the Gauss-Newton techniques [2], which may be computationally costly and require good initialization in order to avoid converging to the local minima of the loss function [2].”
Positioning configurations with the lowest GDOP and their classification (2014, methodology): “There have been thousands of GDOP simulations in positioning system performance analysis in GNSS constellation optimization, mobile positioning, indoor positioning and underwater positioning (Akyildiz et al. 2005; Gustafsson and Gunnarsson 2005; Alcocer et al. 2006; Dempster 2006; Sharp et al. 2012)”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “There exist a plethora of state-of-the-art techniques for static target localization, target tracking, navigation, and interested readers can refer to [8]–[10] and the references therein for more information.”