Evaluation of the Cost-Effective Indoor Wireless Positioning System Using RSSI Method

Drozd, Stanislav; Tomlain, Juraj; Marko, Miroslav; Teren, Ondrej; Tomlain, Jan

doi:10.23919/ntsp54843.2022.9920440

Cited by 2 publications

(3 citation statements)

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Section: Evaluationsmentioning

confidence: 92%

“…[45] used an MLP with a CNN. The highest performance without any combined technology was found in [46], but it had a relatively high standard deviation, 75 cm, compared to the proposed scheme, which presented 25.1 cm, as shown in Figure 13. For further evaluations, the proposed model was trained and tested in another experiment under an extra environment, as shown in Figure 14.…”

Section: 23 X For Peer Reviewmentioning

confidence: 93%

“…[ 45 ] employed AoA with multiple antennas. We note that the number of cells in [ 46 ] could not be presented and were denoted as N/A because they approached trilateration. In addition, from the perspective of ML, Ref.…”

Section: Evaluationsmentioning

confidence: 99%

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Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

Kim

Lee

2023

Sensors

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In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

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Section: Evaluationsmentioning

confidence: 92%

Section: 23 X For Peer Reviewmentioning

confidence: 93%