Validation of ADS-B Aircraft Flight Path Data Using Onboard Digital Avionics Information

Abstract

The adoption of Automatic Dependent Surveillance-Broadcast (ADS-B) transponders has given researchers the ability to capture and record aircraft position data. However, due to the ADS-B system's characteristics, missing data may occur due to propagation anomalies and suboptimal aircraft orientation with respect to the ground-based receiver. The nature of general aviation operations exacerbates this problem. As a result, it may be difficult to accurately review a general aviation aircraft’s flight path with an adequate level of precision. To mitigate this, a five-dimensional modified Unscented Kalman Filter (UKF) was developed to produce statistically optimal aircraft position approximations during all flight phases. The researchers validated the UKF algorithm by comparing estimated flight paths to flight data logs from the Garmin G1000 flight instrument systems of Piper Archer aircraft used in flight training operations on February 23, 2021 at the Purdue University Airport (KLAF). Root mean square error (RMSE) was used to measure the filter’s accuracy. The filter was found to accurately compensate for missing data. This research details the formulation, implementation, and validation of the filtering algorithm.