CNN based approach for Indoor Positioning Services using RSSI Fingerprinting Technique

Hassen, Wiem Fekih; Mezghani, Jihene

doi:10.1109/iwcmc55113.2022.9824987

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The propagation recession of RSSI signals in indoor scenarios and the effects of obstacle occlusion are prone to the problem of noise generation [40]. Hassen et al [41] suggested that Principal Component Analysis (PCA) can efficiently alleviate the impact of noise resulting from signal attenuation in WIFI propagation. In the RSSI signal, there is a correlation between the collected measured signal intensities.…”

Section: Pcamentioning

confidence: 99%

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Zhou,

Yang,

Deng

et al. 2024

Eng. Res. Express

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With the popularization and application of WiFi technology, precise location in multi-layer indoor environments play an increasingly important role in current public places. However, there are still problems such as noise and multi-path effects caused by the propagation degradation of the Received Signal Strength Indicator (RSSI) in indoor scenarios and the shading of obstacles, which lead to a reduction in indoor location accuracy. In this paper, we propose a Vision Transformer indoor localization (VTIL) algorithm based on RSSI and Vision-Transformer (ViT). First, the RSSI fingerprint is normalized to the maximum and minimum values, and the Principal Component Analysis (PCA) method is used for feature extraction to reduce the influence of noise or unimportant features. Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our method reduces the average distance estimation error by 37.26% compared with the currently known indoor fingerprint localization methods.

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Section: Pcamentioning

confidence: 99%

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Zhou,

Yang,

Deng

et al. 2024

Eng. Res. Express

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“…Since this type of signature is more complicated, a particle filter is used to acquire the location. [15] treats RSSI signatures as a high-dimensional image and trains a CNN (convolution neural network) to learn the feature. However, the generalization of the aforementioned fingerprinting/learning approaches is not validated, and the model may be retrained if the environment changes.…”

Section: A Localization Using Wifi Rssimentioning

confidence: 99%

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Shi,

Li,

Tang

et al. 2024

IEEE J. Select. Areas Commun.

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Indoor localization using WiFi signals has been studied since the emergence of WiFi communication. This paper presents a novel training-free approach to indoor localization using a customized WiFi protocol for data collection and a factor graph-based back-end for localization. The protocol measures the round-trip phase, which is very sensitive to small changes in displacement. This is because the sub-wavelength displacements introduce significant phase changes in WiFi signal. However, the phase cannot provide absolute range information due to angle wrap. Consequently, it can only be used for relative distance (displacement) measurement. By tracking the round-trip phase over time and unwrapping it, a relative distance measurement can be realized and achieve a mean absolute error (MAE) of 0.06m. For 2-D localization, factor graph optimization is applied to the round-trip phase measurements between the STA (station) and four APs (access points). Experiments show the proposed concept can offer a decimeter-level (0.26m MAE and 0.24m 50%CDF) performance for real-world indoor localization.

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Survey of Deep Learning Applications in WiFi Localization

Wang,

Cao,

Gong

et al. 2024

2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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CNN based approach for Indoor Positioning Services using RSSI Fingerprinting Technique

Cited by 4 publications

References 9 publications

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization

Survey of Deep Learning Applications in WiFi Localization

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