Calibration-Free Approaches for Robust Wi-Fi Positioning against Device Diversity: A Performance Comparison

Fang, Shih-Hau; Wang, Chu-Hsuan; Chiou, Sheng-Min; Lin, Po-Chiang

doi:10.1109/vetecs.2012.6240088

Cited by 46 publications

(23 citation statements)

References 20 publications

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“…Previous approaches for handling the variance among RSS values fall into two categories: manual calibration approaches based on device mapping [13][14][15] and calibrationfree approaches based on constructing new positioning fingerprint [16][17][18]. [16] proposed a novel positioning method, namely, DIFF, which utilizes the RSS differences between pairs of APs instead of absolute RSS values to construct fingerprinting map, but this method may suffer from dealing with a space of large dimensions according to [17].…”

Section: Related Workmentioning

confidence: 99%

Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Zheng

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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Wi-Fi fingerprinting has become a promising solution for indoor positioning with the rapid deployment of WLAN and the growing popularity of mobile devices. In fingerprint-based positioning, the received signal strengths (RSS) from WLAN access points (APs) usually are regarded as positioning fingerprint to label physical location. However, the RSS variance caused by heterogeneous devices and dynamic environmental status will significantly degrade the positioning accuracy. In this paper, we first show the RSS variance based on a real dataset and analyze the relation existing in the RSS raw values. Then, we utilize both the raw RSS values and their relation to construct a new stable and robust fingerprint for indoor positioning. Experiment results indicate that our method can solve the RSS variance problem without any manual calibration.

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Section: Related Workmentioning

confidence: 99%

Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Zheng

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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“…The concept of Activity Pedestrian SLAM (see Figure 1) for fundamental system architecture) regards positioning determination and mapping across all environments [17][18][19][20][21]. It usually depends upon a multi-sensor setup while augmenting standalone positioning with other signals, motion sensors, and environmental features.…”

Section: Concept Of Activity Pedestrian Slammentioning

confidence: 99%

Optimization of Rao-Blackwellized Particle Filter in Activity Pedestrian Simultaneously Localization and Mapping (SLAM): An Initial Proposal

Bejuri

Mohamad²,

Raja³

et al. 2015

IJSIA

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“…One can see the offset value follows a Gaussian-like distribution centered around −20dB. Such a large gain offset can lead to bad localization results if the database is constructed with one device and directly used by another [13,14,29]. One solution is to measure the hardware gain offset between any pair of devices, which, however, is not scalable [29].…”

Section: Device Diversity Handlingmentioning

confidence: 99%

Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service

Shen

Zhao

et al. 2014

Proceedings of the 20th Annual International Conference on Mobile Computing and Networking

100

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Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach.Realizing the fundamental fact that the quality of the location database capturing the actual radio map dictates localization accuracy, in this paper, we propose Modellet, an algorithmic approach that optimally approximates the actual radio map by unifying modelbased and fingerprint-based approaches. Modellet represents the radio map using a fingerprint-cloud that incorporates both measured real fingerprints and virtual fingerprints, which are computed from models with a local support, based on the key concept of the supporting set. We evaluate Modellet with data collected from an office building as well as 13 large-scale deployment venues (shopping malls and airports), located across China, U.S., and Germany. Comparing Modellet with two representative baseline approaches, RADAR and EZPerfect, demonstrates that Modellet effectively adapts to different data densities and environmental conditions, substantially outperforming existing approaches.

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Calibration-Free Approaches for Robust Wi-Fi Positioning against Device Diversity: A Performance Comparison

Cited by 46 publications

References 20 publications

Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Optimization of Rao-Blackwellized Particle Filter in Activity Pedestrian Simultaneously Localization and Mapping (SLAM): An Initial Proposal

Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service

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