Analysis of wireless geolocation in a non-line-of-sight environment

By Yihong Qi, Hisashi Kobayashi, Hirohito Suda. IEEE Transactions on Wireless Communications, 2006

532 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5bdfbb6b37079f0950011cd8e0668023beedd7d0

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Citations in the corpus

A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques (2009, methodology): “Then, an extended version of the FIM in (19) is given by [42]”
A Survey of Positioning Systems Using Visible LED Lights (2018, methodology): “This system generated prior information of the NLOS distribution using NLOS propagation-induced path lengths modeled by gamma distribution [64].”
A Survey on Wireless Position Estimation (2008, methodology): “For RSS based positioning systems, the MMSE is expressed as [8]”
High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend (2016, background): “where the square root of the right-hand side is defined as the position error bound (PEB), pt is the estimated position, and Jp is the equivalent Fisher information matrix (EFIM) [29]– [32].”
A Theoretical Foundation of Network Localization and Navigation (2018, unknown): “[152] Y.”
Target Localization Accuracy Gain in MIMO Radar-Based Systems (2008, background): “The localization accuracy performance is eva luated in [27].”
Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks (2009, methodology): “Finally, in Section VI we present the results obtained by the methods introduced with several simulations and real measurements carried out in the Higher Technical School of Telecommunications of the University of Valladolid, Spain.”
Fundamental Limits of Wideband Localization— Part II: Cooperative Networks (2010, methodology): “For instance, the ranging error of the time-of-arrival (TOA ) metric is commonly modeled as additive Gaussian [27], [28], [31].”
Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks (2005, background): “It can be shown, for AWGN channels, that a set of TOAs determined from the matched filter outputs is a sufficient statistic for obtaining the MLE or maximum a posteriori (MAP) probability estimate of the location of the node in question (see [29]–[32]).”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, background): “However, it is known that NLoS anchor nodes cannot contribute to the improvement of localization accuracy, and can even cause interference for localization when no prior information on their NLoS paths is available [217].”
Fundamental Limits of Wideband Localization— Part I: A General Framework (2010, methodology): “noise injected in the true propagation distances [8], [53], [54];3 2) identify and weigh the importance of NLOS signals for localization [55]–[60]; or 3) consider NLOS biases as param eters to be estimated [27]–[30], [46], [47], [61], [62].”
Position Estimation via Ultra-Wide-Band Signals (2008, background): “quite close for sufficiently high signal bandwidths and/or SNRs [19], [20].”
NLOS Identification and Weighted Least-Squares Localization for UWB Systems Using Multipath Channel Statistics (2008, background): “As discussed in [39], if no prior knowledge regarding the NLOS bias bi is available, the Cramer-Rao lower bound (CRLB) is minimized by discarding the NLOS FTs (assuming perfect identification of LOS/NLOS measurements).”