Assessment of Android Network Positioning as an Alternate Source for Robust PNT

Area of Science:

• Engineering
• Computer Science
• Geomatics

Background:

• Smartphone positioning relies on various methods, with Network Location Provider (NLP) using Wi-Fi and cell towers.
• NLP offers potential for robust Positioning, Navigation, and Timing (PNT) solutions, especially indoors or during Global Navigation Satellite System (GNSS) radio frequency interference (RFI).
• The internal operations of NLP are largely unknown, functioning as a 'black-box' system.

Purpose of the Study:

• To investigate the operational characteristics of Android's Network Location Provider (NLP) under adverse conditions.
• To explore NLP's response to GNSS spoofing and offline network scenarios.
• To uncover the internal mechanisms of NLP for enhanced PNT solutions.

Main Methods:

• Testing NLP performance on Samsung S24 and Xiaomi Redmi K80 Ultra devices.
• Simulating GNSS spoofing to observe NLP's positional accuracy.
• Evaluating NLP functionality during offline operation without network connectivity.
• Attempting to spoof NLP itself.

Main Results:

• NLP maintained accurate positioning at the true location even when GNSS signals were spoofed, confirming its robustness against RFI.
• NLP continued to function in offline mode, utilizing internally cached data without requiring a live server connection.
• Observed offline functionality deviates from the conventional understanding of NLP's reliance on external servers.

Conclusions:

• Android's NLP demonstrates significant robustness against GNSS spoofing and RFI.
• NLP possesses local data processing capabilities, enabling offline positioning through cached information.
• These findings provide crucial insights into NLP's internal workings, contributing to the development of more reliable smartphone PNT systems.