Bayesian indoor positioning systems

Madigan, David; Einahrawy, E.; Martin, Richard P.; Ju, Wen-Hua; Krishnan, P.; Krishnakumar, A. S.

doi:10.1109/infcom.2005.1498348

Cited by 318 publications

(256 citation statements)

References 23 publications

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“…Bisio et al [71] proposed an intelligent probabilistic fingerprinting algorithm that improved the computational speed of prior probabilities and reduced the cost of positioning and the time-consuming costs associated with traditional probabilistic fingerprinting algorithms. Madigan et al [72] used a Bayesian hierarchical model for positioning.…”

Section: Probabilistic Positioning Algorithmmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

197

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: Probabilistic Positioning Algorithmmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

197

125

View full text Add to dashboard Cite

show abstract

“…Another localization method examines the received signal strength (RSS) of a message broadcast from a known location [52], [53]. Since the free-space signal strength model is governed by the inverse-square law, accurate localization is possible.…”

Section: Measurement Phasementioning

confidence: 99%

“…Such methods are used in [55], [53], and [70]. Like MLE, the data likelihood is computed using a measurement model.…”

Section: Cellularmentioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

253

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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“…Additionally, a Bayesian model for the noisy distance measurements was reported in [56]. The model is demonstrated in Figure 9.16.…”

Section: Statistical Techniquesmentioning

confidence: 99%

“…[56] A hierarchical Bayesian graphical model is brought up in order to incorporate the prior knowledge of linear regression models in accordance with the access points that the coefficients of the models are similar to each other. The conditional density for the vertexes are: where Gamma represents the Gamma distribution.…”

Section: Statistical Techniquesmentioning

confidence: 99%

Algorithmic Aspects of Sensor Localization

Das

Wang

Ghosh

et al. 2010

Monographs in Theoretical Computer Science. An EATCS Series

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Identifying locations of nodes in wireless sensor networks (WSNs) is critical to both network operations and most application level tasks. Sensor nodes equipped with geographical positioning system (GPS) devices are aware of their locations at a precision level of few meters. However, installing GPS devices on a large number of sensor nodes is not only expensive but affects the form factor of these nodes. Moreover, GPS-based localization is not applicable in the indoor environments such as buildings. There exists an extensive body of research literature that aims at obtaining absolute locations as well as relative spatial locations of nodes in a WSN without requiring specialized hardware at large scale. The typical approach consists of employing only a limited number of anchor nodes that are aware of their own locations, and then trying to infer locations of non-anchor nodes using graph theoretic, geometric, statistical, optimization and machine learning techniques. Thus, the literature represents a very rich ensemble of algorithmic techniques applicable to low power, highly distributed nodes with resource-optimal computations. In this chapter we take a close look at the algorithmic aspects of various important localization techniques for WSNs.

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Bayesian indoor positioning systems

Cited by 318 publications

References 23 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

A Survey on Localization for Mobile Wireless Sensor Networks

Algorithmic Aspects of Sensor Localization

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