Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF

The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5891
Main Authors: Yang, Jingmin, Deng, Shanghui, Xu, Li, Zhang, Wenjie
Format: Journal Article
Language:English
Published: Basel MDPI AG 07.08.2022
MDPI
Subjects:
3D indoor positioning
Accuracy
Algorithms
Calibration
calibration-free
deep denoising autoencoder (DDAE)
Deep learning
fingerprint database
Global positioning systems
GPS
Location based services
Machine learning
Methods
Radio frequency identification
signal strength difference (DIFF)
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution for handling the received signal strength variance between diverse devices, can effectively reduce the negative impact of signal fluctuation. However, DIFF also leads to the explosion of the RSSI data dimension, expanding the number of dimensions from m to Cm2, which reduces the positioning efficiency. To this end, we design a data hierarchical processing strategy based on a building-floor-specific location, which effectively improves the efficiency of high-dimensional data processing. Moreover, based on a deep neural network (DNN), we design three different positioning algorithms for multi-building, multi-floor, and specific-location respectively, extending the indoor positioning from the single plane to three dimensions. Specifically, in the stage of data preprocessing, we first create the original RSSI database. Next, we create the optimized RSSI database by identifying and deleting the unavailable data in the RSSI database. Finally, we perform DIFF processing on the optimized RSSI database to create the DIFF database. In the stage of positioning, firstly, we design an improved multi-building positioning algorithm based on a denoising autoencoder (DAE). Secondly, we design an enhanced DNN for multi-floor positioning. Finally, the newly deep denoising autoencoder (DDAE) used for specific location positioning is proposed. The experimental results show that the proposed algorithms have better positioning efficiency and accuracy compared with the traditional machine learning algorithms and the current advanced deep learning algorithms.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22155891