Terminal Group Mobility Geolocation for Lower Calibration Burden

The patent combines multiple data sources (radio signatures, sensor data from accelerometers, gyroscopes, barometers, and GPS) into a unified geolocation framework. By merging these diverse inputs and processing them through a single machine learning model, the system achieves higher accuracy while avoiding the complexity of multiple separate systems.

The machine learning model serves multiple functions: it processes radio signatures, sensor data, and GPS information; it performs both calibration and geolocation tasks; and it adapts to different environmental conditions. This multi-functionality reduces the need for separate specialized systems.

The system performs calibration in advance by collecting radio signatures and sensor data at multiple locations before actual geolocation operations begin. This preliminary calibration phase creates a database that can be reused for subsequent geolocation tasks, avoiding repeated calibration efforts.

The patent uses sensor data and radio signatures collected during calibration as templates or copies that represent specific locations and conditions. During operation, the system compares current measurements against these stored copies to determine geolocation, eliminating the need for real-time recalibration.

The system extracts and uses only the most relevant features from the collected data, such as key sensor measurements and radio signature characteristics. By extracting essential information rather than processing all raw data, the system maintains accuracy while reducing computational burden.

The machine learning model transforms raw sensor data and radio signatures into optimized parameter representations that are more efficient to process. By changing the parameter space and using dimensionality reduction techniques, the system handles large datasets with reduced computing requirements.