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IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

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An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning.

Donghyun Kim¹, Kyuho Son¹, Dongsoo Han¹

¹School of Computing, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This study introduces an adaptive algorithm for Wi-Fi fingerprinting indoor positioning. It improves location tracking stability with irregularly collected data, enhancing location-based services.

Keywords:

adaptive algorithm indoor positioning location based service passive fingerprinting user tracking

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Area of Science:

Computer Science
Electrical Engineering
Ubiquitous Computing

Background:

Wi-Fi fingerprinting, using received signal strength (RSS) values, is a leading indoor positioning technique.
Passive fingerprinting offers user-independent location tracking by utilizing smartphone-emitted signals.
Irregular data collection due to user network activity poses a challenge for passive Wi-Fi fingerprinting.

Purpose of the Study:

To propose an adaptive algorithm for stable indoor positioning with irregularly sampled Wi-Fi fingerprints.
To address the limitations of inconsistent data collection in passive Wi-Fi fingerprinting.
To enhance the reliability of indoor location tracking.

Main Methods:

Development of an adaptive algorithm designed for handling fingerprints collected at irregular time intervals.
Utilizing received signal strength (RSS) values for vector-type fingerprinting.
Experimental validation in three distinct indoor scenarios.

Main Results:

The proposed adaptive algorithm demonstrated stable tracking performance even with irregular data intervals.
Experimental results verified the accuracy and stability of the developed tracking method.
The method effectively mitigates issues arising from non-uniform data collection.

Conclusions:

The adaptive algorithm significantly improves the stability of Wi-Fi fingerprinting-based indoor positioning.
Enhanced indoor positioning stability is expected to improve the quality of location-based services.
This research offers a robust solution for passive indoor tracking challenges.