ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information

Chen, Hao; Zhang, Yifan; Li, Wei; Tao, Xiaofeng; Zhang, Ping

doi:10.1109/access.2017.2749516

Cited by 254 publications

(166 citation statements)

References 24 publications

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“…Our average accuracy is also almost twice that of RADAR [12]. If we consider frameworks that used CSI (DeepFi [14] and ConFi [15]), our accuracy is very close to both at just under 2 meters. However, [14] and [15] use special equipment to capture CSI and cannot be used with mobile devices.…”

Section: Accuracy Analysis With Other Approachesmentioning

confidence: 77%

“…If we consider frameworks that used CSI (DeepFi [14] and ConFi [15]), our accuracy is very close to both at just under 2 meters. However, [14] and [15] use special equipment to capture CSI and cannot be used with mobile devices. In contrast, our proposed CNN-LOC framework is easy to deploy on today's smartphones, does not require any specialized infrastructure (e.g., custom beacons), and can be used in buildings wherever Wi-Fi infrastructure pre-exists.…”

Section: Accuracy Analysis With Other Approachesmentioning

confidence: 99%

“…HMM predictions are also based on the previous position acquired through the DNN and hence, can be prone to error accumulation. DeepFi [14] and ConFi [15] propose approaches that use the Channel State Information (CSI) of Wi-Fi signals to create fingerprints. But the CSI information in these approaches was obtained through the use of specialized hardware attached to a laptop.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

Mittal

Tiku

Pasricha

2018

Proceedings of the 2018 Great Lakes Symposium on VLSI

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Indoor localization is emerging as an important application domain for enhanced navigation (or tracking) of people and assets in indoor locales such as buildings, malls, and underground mines. Most indoor localization solutions proposed in prior work do not deliver good accuracy without expensive infrastructure (and even then, the results may lack consistency). Ambient wireless received signal strength indication (RSSI) based fingerprinting using smart mobile devices is a low-cost approach to the problem. However, creating an accurate 'fingerprinting-only' solution remains a challenge. This paper presents a novel approach to transform Wi-Fi signatures into images, to create a scalable fingerprinting framework based on Convolutional Neural Networks (CNNs). Our proposed CNN based indoor localization framework (CNN-LOC) is validated across several indoor environments and shows improvements over the best known prior works, with an average localization error of < 2 meters.

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Section: Accuracy Analysis With Other Approachesmentioning

confidence: 77%

Section: Accuracy Analysis With Other Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

Mittal

Tiku

Pasricha

2018

Proceedings of the 2018 Great Lakes Symposium on VLSI

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“…The efficacy of deep learning has been explored recently for wireless positioning [19]- [21]. The methods in these papers rely on extensive measurement campaigns and require CSI measurements annotated with exact position information.…”

Section: Relevant Prior Artmentioning

confidence: 99%

Improving Channel Charting with Representation -Constrained Autoencoders

Huang

Castañeda

Gönültaş

et al. 2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.

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“…RSSI provides the signal power received at the receiver and can be retrieved from most Wi-Fi receivers such as mobile phones, tablets, laptops, etc. [4] RSSI has two drawbacks: instability due to fading and multipath [5] and the lack of information. On the other hand, CSI has higher granularity than the RSSI as it can capture both the amplitude and phase responses of the channel in different frequencies and between separate transmitter-receiver antennae pairs [2].…”

Section: Introductionmentioning

confidence: 99%

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

et al. 2020

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This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% -30%.Index Terms-Received signal strength indicator (RSSI), channel state information (CSI), WiFi indoor localization, probabilistic approach based localization.

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ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information

Cited by 254 publications

References 24 publications

Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

Improving Channel Charting with Representation -Constrained Autoencoders

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

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