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Updated: May 17, 2025

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An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE.

Jianming Li¹, Shuyan Yu², Zhe Wei¹

¹School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

This study introduces a robust ZigBee localization method using advanced signal filtering and dynamic weighting for improved indoor positioning accuracy. The approach enhances reliability in challenging wireless sensor network environments.

Keywords:

RSSI filtering ZigBee indoor positioning link quality indicator maximum likelihood estimation

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Published on: February 8, 2019

Area of Science:

Wireless Sensor Networks
Localization and Positioning
Signal Processing

Background:

Indoor localization in wireless sensor networks faces challenges from signal variability and multipath propagation.
Accurate positioning is crucial for many IoT and sensor network applications.
Existing methods struggle with complex indoor environments and signal degradation.

Purpose of the Study:

To develop a robust ZigBee-based indoor localization approach.
To enhance positioning accuracy and consistency in non-line-of-sight (NLOS) conditions.
To address signal variability and multipath propagation issues.

Main Methods:

Multi-stage preprocessing of Received Signal Strength Indicator (RSSI) data.
Hybrid filtering framework: Kalman filtering, Dixon's Q test, Gaussian smoothing, mean averaging.
Reliability-aware Maximum Likelihood Estimation (MLE) with dynamic weighting based on noise and Link Quality Indicator (LQI).

Main Results:

Improved measurement stability and reduced influence of noise and outliers.
Enhanced positioning consistency in simulated and semi-physical NLOS indoor conditions.
Achieved an average accuracy gain of 11.7% over conventional MLE.

Conclusions:

The proposed method offers a feasible solution for resource-constrained localization.
The approach demonstrates robustness to signal degradation in complex indoor environments.
Further validation in diverse real-world scenarios is recommended.