Soft Information for Localization-of-Things

Conti, Andrea; Mazuelas, Santiago; Bartoletti, Stefania; Lindsey, W. C.; Win, Moe Z.

doi:10.1109/jproc.2019.2905854

Cited by 160 publications

(65 citation statements)

References 210 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, the positioning errors have been measured to be relatively high on the edges, or corners, of the experimental environment. This may be caused by the multipath effect [12], [52]. Therefore, in order to boost the positioning accuracy, it is absolutely necessary to analyze the characteristics of practical indoor optical wireless channel in near future.…”

Section: Discussionmentioning

confidence: 99%

5G Internet of Radio Light Positioning System for Indoor Broadcasting Service

Shi

Zhang

et al. 2020

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

Many features of 5G are definitely important to the broadcasting service, including diverse content services such as follow-me TV, video-on-demand, but also gaming, Virtual reality (VR) and Augmented reality (AR) and many others. Meanwhile, those services depend more and more on the user's position accuracy, especially in indoor environment. With the increase of broadcasting data traffic indoors, to obtain a highly accurate position is becoming a challenge because of the impact of radio interference. In order to support a high-quality indoor broadcasting service, a high-accuracy positioning, radiation-free, and high-capacity communication system is urgently needed. In this paper, a 5G indoor positioning system is proposed for museums. It utilizes unlicensed visible light of the electromagnetic spectrum to provide museum visitors with high-accuracy positioning, multiple forms of interaction services, and highresolution multimedia delivery on a mobile device. The geographic data and the location-related data integrated into the 5G New Radio (NR) waveform are detailed. A general-purpose system architecture is provided and some basic techniques to enhance system performance are also investigated. A prelimi

show abstract

Section: Discussionmentioning

confidence: 99%

5G Internet of Radio Light Positioning System for Indoor Broadcasting Service

Shi

Zhang

et al. 2020

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

show abstract

“…In practice, numerous localization algorithms have been developed [27], [41], which can be categorized by the location of estimation into centralized [42] and decentralized algorithms [27], [43]- [45]; by the extractable position-related signal features into signal power, carrier phase or symbol delay-based algorithms [17], [18], [46]; by the measurement of abstraction level into direct localization [47]- [49] and a two-stage approach [17]; by the model of unknown parameters into non-Bayesian algorithms for deterministic parameters like least-square (LS), Gauss-Newton algorithm [46], convex-relaxationbased approaches such as semi-definite programming (SDP) [50] and alternating direction method of multipliers (ADMM) [42], or Bayesian algorithms for random variables [51] like Kalman filter (KF) [45], particle filter (PF) [52], [53], or message passing (MP) algorithms [27], [48], [54].…”

Section: A Research Related To Swarm Localizationmentioning

confidence: 99%

Self-Aware Swarm Navigation in Autonomous Exploration Missions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…3 This paper focuses on range-related measurements, however the methodology introduced here can analogously be used for measurements related to other positional features including angle, velocity, and acceleration. For a general positional feature θ, the corresponding soft information (SI) of a θ-related measurements set y would be a function of θ proportional to f (y|θ) [47]. 1 In this paper, measurements set refers to a collection of observations, possibly of different types.…”

Section: Soft Range Informationmentioning

confidence: 99%

Soft Range Information for Network Localization

Mazuelas

Conti

Allen

et al. 2018

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramér-Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.

show abstract

Soft Information for Localization-of-Things

Abstract: Soft information is much richer than single-value estimates. Its exploitation opens the way to a new level of accuracy for the Localization-of-Things.

Cited by 160 publications

References 210 publications

5G Internet of Radio Light Positioning System for Indoor Broadcasting Service

5G Internet of Radio Light Positioning System for Indoor Broadcasting Service

Self-Aware Swarm Navigation in Autonomous Exploration Missions

Soft Range Information for Network Localization

Contact Info

Product

Resources

About