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

Gustafsson, Fredrik; Gunnarsson, Fredrik

doi:10.1109/msp.2005.1458284

Cited by 882 publications

(457 citation statements)

References 38 publications

Supporting

Mentioning

432

Contrasting

Unclassified

Order By: Relevance

“…In past years, various techniques based on TOA, TDOA, AOA, and signal strength have been considered for localization in cellular networks [219]. However, their accuracy was heavily limited to hundreds and tens of meters due to the severe channel impairments (i.e., multipath, shadowing, NLoS propagation) between the BS and mobile and insufficient bandwidth and received signal power.…”

Section: G Localizationmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

View full text Add to dashboard Cite

Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

show abstract

Section: G Localizationmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

View full text Add to dashboard Cite

show abstract

“…The three-dimensional coordinates of the APs are known parameters [11]. Symbol d is known fixed distance between the two AP, h is the AP height from the ground.…”

Section: Composite Positioning Methods (Toa and Aoa)mentioning

confidence: 99%

Research on the Key Technology and Method of Indoor Localization Based on WLAN

Chen¹,

He²,

Yang³

2014

JACN

View full text Add to dashboard Cite

“…The Cramér-Rao Lower Bound (CRLB), which gives the minimum variance of unbiased estimators, is widely used as a measure of the precision attainable for parameter estimates from a given set of observations (Van Trees et al, 1968 andKay, 1993). The CRLB was compared with the mean square errors of different localization algorithms under low SNR conditions (Gustafsson andGunnarsson, 2005 andMacagnano et al, 2012). Using TOA and TDOA measurements, So (2011) used the corresponding Fisher information matrix with zero-mean Gaussian distributed measurement errors to compute CRLB in LOS scenarios.…”

Section: B Theoretical Analysismentioning

confidence: 99%

“…has been a subject of research for decades and continues to receive much interest in the signal processing research community, including radar (Bahl andPadmanabhan, 2000 andYan et al, 2007), sonar (Carter, 1981;Leonard and Durrant-Whyte, 1991;and Leonard and Durrant-Whyte, 2012), mobile communications (Caffery, 2000;Gustafsson and Gunnarsson, 2005;and Caffery and Stuber, 1998), multimedia (Brandstein and Silverman, 1997;Wang and Chu, 1997;and Akyildiz et al, 2007), animal tracking (Spiesberger and Fristrup, 1990), wireless sensor networks (WSNs; Akyildiz et al, 2002;Chen et al, 2002;Patwari et al, 2005;and Mao et al, 2007), and the Global Positioning System (GPS; Hofmann-Wellenhof et al, 2013). However, uncertainties resulting from the modulation of a signal propagated through an inhomogeneous medium and in measurements of received signals can quickly degrade position estimation accuracy when using standard methods to estimate transmitter position (Sayed et al, 2005;Güvenç and Chong, 2009;and Tan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

118

View full text Add to dashboard Cite

Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

show abstract

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

Cited by 882 publications

References 38 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Research on the Key Technology and Method of Indoor Localization Based on WLAN

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Contact Info

Product

Resources

About