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Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors.

Sira Yongchareon¹, Jian Yu¹, Jing Ma¹

¹School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This summary is machine-generated.

This study introduces a new deep learning method for device-free indoor localization using Passive Infrared (PIR) sensors. The novel approach accurately estimates the locations of multiple individuals, enhancing safety and energy management applications.

Keywords:

PIRs deep learning-based localization device-free indoor localization multi-person localization

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Device-free indoor localization is essential for applications like healthcare, safety, and energy management.
Passive Infrared (PIR) sensors offer a cost-effective, low-power, and privacy-preserving solution for human localization.
Existing multi-person localization methods struggle with signal quality, ambiguity, and interference from complex movements.

Purpose of the Study:

To propose a novel deep learning method for accurate multi-person indoor localization using PIR sensors.
To address the limitations of current methods in handling complex human movements and signal interference.

Main Methods:

A deep Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is employed.
Channel separation and template-matching techniques are utilized for signal processing.
Ensemble models with a mean bagging technique enhance localization accuracy.

Main Results:

The proposed method successfully estimates the simultaneous locations of two participants.
Achieved a mean distance error of 0.55 meters for localization.
80% of distance errors were within 0.8 meters, demonstrating high accuracy.

Conclusions:

The novel deep learning approach significantly improves multi-person localization accuracy using PIR sensors.
This method offers a robust solution for device-free indoor localization in complex environments.
The findings have implications for enhancing smart environments in healthcare, safety, and energy management.