CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 21; H. 4; S. 1114
Hauptverfasser: Qin, Feng, Zuo, Tao, Wang, Xing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 05.02.2021
MDPI AG
Schlagworte:
convolutional denoising autoencoder
convolutional neural network
K-means
WiFi fingerprint positioning
K-means
convolutional denoising autoencoder
convolutional neural network
WiFi fingerprint positioning
ISSN:1424-8220, 1424-8220
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Zusammenfassung:WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.
Bibliographie:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21041114