Abstract
The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.
Original language | English |
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Article number | 989 |
Journal | Electronics (Switzerland) |
Volume | 8 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2019 |
Keywords
- AlexNet
- CNN
- Fingerprint
- Inception v3
- Indoor localization
- MobileNet v2
- ResNet
- ZFNet