Statistical path loss parameter estimation and positioning using RSS measurements in indoor wireless networks

Nurminen, Henri; Talvitie, Jukka; Ali-Löytty, Simo; Müller, Philipp L.; Lohan, Elena Simona; Piché, Robert; Renfors, Markku

doi:10.1109/ipin.2012.6418856

Cited by 45 publications

(37 citation statements)

References 12 publications

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“…The radar [2] system analyzed the ground attenuation factor channel model (FAF) [46], and found that when this model was associated with a large-scale path loss model, it was flexible enough to be used in different indoor space structures. Based on the FAF, researchers built a model called the wall attenuation factor channel model (WAF) used in radar, as shown in Equation (5).…”

Section: Application Of Channel Propagation Modelmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

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: Application Of Channel Propagation Modelmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

View full text Add to dashboard Cite

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“…Using calculations and appropriate positioning algorithm (usually trilateration), the geometrical parameters then compute the position of an object (Ibid.). For example, the studies by Mazuelas et al (2009) and Nurminen et al (2012) employ the propagation method. While Mazuelas et al (2009) made use of RSS values and trilateration to determine position, Nurminen et al (2012) used RSS, Bayesian statistical methods and a different set of algorithms, namely "Metropolis-Hastings and Gauss-Newton" algorithms, to determine position dynamically or real-time.…”

Section: Wlan-based Positioning Systemmentioning

confidence: 99%

“…For example, the studies by Mazuelas et al (2009) and Nurminen et al (2012) employ the propagation method. While Mazuelas et al (2009) made use of RSS values and trilateration to determine position, Nurminen et al (2012) used RSS, Bayesian statistical methods and a different set of algorithms, namely "Metropolis-Hastings and Gauss-Newton" algorithms, to determine position dynamically or real-time. An advantage of these implementations is that they are computationally light (Mazuelas et al, 2009;Nurminen et al, 2012).…”

Section: Wlan-based Positioning Systemmentioning

confidence: 99%

“…While Mazuelas et al (2009) made use of RSS values and trilateration to determine position, Nurminen et al (2012) used RSS, Bayesian statistical methods and a different set of algorithms, namely "Metropolis-Hastings and Gauss-Newton" algorithms, to determine position dynamically or real-time. An advantage of these implementations is that they are computationally light (Mazuelas et al, 2009;Nurminen et al, 2012). However, because RSS is mainly used in propagation, it is difficult to fit the RSS distribution accurately using mathematical formula and it is difficult to estimate the distance due to localisation errors (H. Wang & Jia, 2007).…”

Section: Wlan-based Positioning Systemmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

148

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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“…The general RSS-to-distance conversion approach is by curve fitting with for example, parabolic or logarithmic regression, based on free space propagation model [4]. By further considering the complex real site conditions such as path loss of signal due to attenuation, reflection and refraction, as well as the geometrical effects on length resection, different RSS-to-distance conversion algorithms such as the Gaussian process regression [5] and the statistical path loss parameter estimation [6] were proposed. Regarding the fingerprinting approach that is more suitable for indoor environments, it has the advantage that the APs coordinates are not required in the position determination process.…”

Section: Introductionmentioning

confidence: 99%

An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning

Mok

Cheung

2013

IJGI

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Abstract:Ubiquitous positioning provides continuous positional information in both indoor and outdoor environments for a wide spectrum of location based service (LBS) applications. With the rapid development of the low-cost and high speed data communication, Wi-Fi networks in many metropolitan cities, strength of signals propagated from the Wi-Fi access points (APs) namely received signal strength (RSS) have been cleverly adopted for indoor positioning. In this paper, a Wi-Fi positioning algorithm based on neural network modeling of Wi-Fi signal patterns is proposed. This algorithm is based on the correlation between the initial parameter setting for neural network training and output of the mean square error to obtain better modeling of the nonlinear highly complex Wi-Fi signal power propagation surface. The test results show that this neural network based data processing algorithm can significantly improve the neural network training surface to achieve the highest possible accuracy of the Wi-Fi fingerprinting positioning method.

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Statistical path loss parameter estimation and positioning using RSS measurements in indoor wireless networks

Cited by 45 publications

References 12 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning

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