RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization.

Area of Science:

• Wireless communication
• Indoor localization and positioning
• Signal processing

Background:

• Smart indoor living necessitates low-cost localization systems using Wireless Local Area Networks (WLAN).
• Existing Received Signal Strength Indicator (RSSI) fingerprinting methods are often costly, time-consuming, and sensitive to environmental changes.
• Current techniques do not adequately consider transmitter height or complex indoor signal propagation.

Purpose of the Study:

• To propose and experimentally evaluate a novel RSSI distance prediction technique for enhanced indoor localization.
• To leverage transceiver height and Fresnel ranging to improve RSSI path loss modeling.
• To reduce the cost and complexity of indoor localization systems.

Main Methods:

• Developed and applied a Height Dependence Path-Loss (HEM) model incorporating transceiver height and Fresnel ranging.
• Conducted experiments in complex indoor environments (corridors, offices) during work hours.
• Compared the HEM model's accuracy and error rates against conventional prediction algorithms (NEM, MWM, OSM).

Main Results:

• The HEM model demonstrated significantly improved accuracy and lower average prediction errors in office and corridor environments.
• HEM model achieved higher confidence probabilities for RSSI distance prediction compared to NEM, MWM, and OSM models.
• HEM fingerprint localization outperformed comparison methods, showing a 13% improvement in localization accuracy.

Conclusions:

• The proposed HEM model offers a low-cost, accurate solution for indoor localization by accounting for transceiver height and Fresnel ranging.
• This technique enhances RSSI path loss prediction, leading to more reliable indoor positioning.
• The HEM model provides a robust and efficient alternative to existing indoor localization methods.