Real Time Detection and Estimation of GNSS Interference Affected Region Using ADS-B Data and Bayesian Modeling

Abstract

GNSS serves safety-of-life applications in aviation such as precise navigation for approach and landing operations. Interference events happen near airport can severely affect the safe operations of the airspace. Two events in 2022, one at Dallas-Fort Worth International Airport (KDFW) and another at Denver International Airport (KDEN) caused widespread disruptions resulting in multiple aircraft reporting GPS unreliable within 30-40NM of the airport. Being able to quickly detect the existence of GNSS interference events can help reduce the safety or operational risks caused by such disruptions. This paper examines the use of Automatic Dependent Surveillance—Broadcast (ADS-B) to detect GNSS radio frequency interference (RFI). ADS-B is a surveillance system which has aircraft broadcasting its position every 0.4 – 0.6 sec. The position message contains a quality indicator which describes the accuracy and integrity of GPS performance. Depending on the severity of the interference experienced, ADS-B might stop broadcasting or report low position quality. Either result can be used to identify existence of GPS interference. ADS-B is already widely in use by commercial aircraft and there are many companies and academic networks that receive and offer ADS-B data. The ubiquity and openness of ADS-B provides an available widespread source of GNSS information. This study aims to develop an algorithm which can perform rapid detection of GNSS interference events using filtered ADS-B data. The algorithm needs to be able to detect multiple concurrent events while minimizing false alerts. The ultimate goal is to design a system of algorithms that is able to monitor the entire U.S. and provide immediate and reliable information about potential GPS interference events. The algorithm was tested and validated using data collected from real interference events.