WiFi fingerprinting signal strength error modeling for short distances

Moghtadaiee, Vahideh; Dempster, Andrew G.

doi:10.1109/ipin.2012.6418852

Cited by 28 publications

(14 citation statements)

References 10 publications

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“…Fingerprint data collection, also called offline training, is the first phase of fingerprint positioning, which uses the fingerprinting technique to store the location dependent characteristics of a signal collected at known locations ahead of the system's use for localization in a database [33]. The main task in this phase is to collect fingerprint data to build a model.…”

Section: Fingerprint Data Collectionmentioning

confidence: 99%

“…Accurate modeling requires the accurate position of the APs; however, the position can be hard to determine in most cases. Thus, channel propagation modeling can be used in an experimental environment where the position of the AP is known to find the relationships between fingerprint data [33], or examine the performance of the proposed method. Furthermore, this modeling can be used in a real positioning environment to calibrate the positioning results obtained by other means.…”

Section: Accurate Modelingmentioning

confidence: 99%

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Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

194

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: Fingerprint Data Collectionmentioning

confidence: 99%

Section: Accurate Modelingmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

194

125

View full text Add to dashboard Cite

show abstract

“…The fingerprint database emulates a lookup table during real time localization. Distance based methods are typically employed to find the nearest reference point [29]. Because reference points must be manually collected offline, fingerprint databases are often time consuming and expensive.…”

Section: W-based Localizationmentioning

confidence: 99%

RGB-W: When Vision Meets Wireless

Alahi

Haque

2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional "quasi-free" modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsitydriven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services.

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“…In classic approach WiFi indoor location is based on fingerprinting [1,[4][5][6][7][8][9][10]. Fingerprinting is a way of representing a Radio Map as a set of metric positions combined with WiFi BSSID's (Basic Service Set Identifier) and their RSSI (Received Signal Strength Indication).…”

Section: Introductionmentioning

confidence: 99%