Dimensionality Reduction Through Multiple Convolutional Channels for RSS-Based Indoor Localization

doi:10.1109/JSEN.2024.3470549

2024-11-15

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DOI：10.1109/JSEN.2024.3470549

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摘要

En 中文

Dimensionality reduction is an important task for Wi-Fi-based indoor localization (IL). Most such techniques do not take into account realistic data collection issues such as the presence of outliers or inconsistent fingerprint instances. These fingerprints either represent a class boundary or an outlier. Instance hardness is a measure that better characterizes such instances. Accordingly, in this work, our contribution is to propose a convolutional autoencoder-based dimensionality reduction approach that works on the basis of feature transformation and instance hardness. The encoding process of the data input involves a two-channel representation of a fingerprint dataset that holds the normalized RSS and an instance hardness measure, that is, a k-disagreeing score. The inclusion of the k-disagreeing score into the training pipeline is made with the objective of injecting instance importance for training using 1-D CNN architectures for classification. The experimentations were performed on three benchmark datasets and a collected dataset. The proposed pipeline is found to yield an accuracy of more than 97% with error deviation ranging from 2.2- 2.37m which is quite acceptable for any localization system.

Keyword:

Feature extraction

Fingerprint recognition

Location awareness

Training

Convolution

Pipelines

Encoding

Vectors

Dimensionality reduction

Wireless fidelity

Autoencoder