Accuracy Assessment of D-Model for Modeling Wall Attenuation in Indoor Environment

Latif, Shiraz; Memon, Aftab Ahmed; Chowdhry, Bhawani Shankar; Zieliński, Ryszard; Ghazanfar, Muhammad

doi:10.1109/cicsyn.2014.28

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Their method results in a reliable localization thanks to the stability of the estimated channel parameters. In [24], S. Latif et al proposed a D-model to estimate the radio signal strength in indoor areas. The experiments in their paper proved that the proposed D-model is caplable of estimating the RSS values with a high accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang¹,

Valaee²,

Xu³

et al. 2017

Preprint

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Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang¹,

Valaee²,

Xu³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Their method results in a reliable localization thanks to the stability of the estimated channel parameters. In [23], S. Latif et al proposed a D-model to estimate the radio signal strength in indoor areas. The experiments in their paper proved that the proposed D-model is capable of estimating the RSS values with a high accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang

Valaee

et al. 2017

Applied Sciences

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Abstract:Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

show abstract

Accuracy Assessment of D-Model for Modeling Wall Attenuation in Indoor Environment

Cited by 2 publications

References 14 publications

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

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