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

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.