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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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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint

Yepeng Ni¹, Jianbo Liu², Shan Liu³

¹Computer and Network Center, Communication University of China, No. 1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China. nyp_2010@cuc.edu.cn.

Sensors (Basel, Switzerland)

|September 13, 2016

Summary

This study introduces a novel indoor positioning method using fuzzy pattern recognition within a Hidden Markov Model. It improves accuracy by analyzing Received Signal Strength Indicator (RSSI) trends, not just values, for better pedestrian tracking.

Keywords:

RSSI variation trend fingerprint system fuzzy pattern recognition algorithm hidden markov model pedestrian positioning smartphone

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Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

Indoor location-based services are increasingly common with smartphone adoption.
Received Signal Strength Indicator (RSSI) variations in indoor environments cause significant positioning errors.
Deterministic positioning methods struggle with dynamic pedestrian movement and signal propagation complexities.

Purpose of the Study:

To enhance indoor pedestrian positioning accuracy.
To address the limitations of deterministic methods in dynamic environments.
To develop a novel approach integrating fuzzy logic and Hidden Markov Models for improved localization.

Main Methods:

Embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model (HMM).
Utilizing RSSI variation trends, correlating fading with distance, for fuzzy positioning.
Training HMM transition probabilities with fuzzy pattern recognition on pedestrian trajectories.
Applying the Viterbi algorithm for extracting hidden location states.

Main Results:

The proposed method significantly improves indoor positioning accuracy compared to deterministic algorithms.
The approach demonstrates robust adaptability to diverse environmental conditions.
Analysis of RSSI trends proved more effective than specific RSSI values for dynamic positioning.

Conclusions:

The integration of fuzzy pattern recognition and HMM offers a superior solution for indoor pedestrian positioning.
The method's reliance on RSSI trends enhances robustness against signal variations.
This approach provides a more accurate and adaptable solution for real-world indoor localization challenges.