UAT ADS-B Data Anomalies and the Effect of Flight Parameters on Dropout Occurrences

Abstract

An analysis of the performance of automatic dependent surveillance-broadcast (ADS-B) data received from the Grand Forks, North Dakota International Airport was carried out in this study. The purpose was to understand the vulnerabilities of the universal access transceiver (UAT) ADS-B system and recognize the effects on present and future air traffic control (ATC) operation. The Federal Aviation Administration (FAA) mandated all the general aviation aircraft to be equipped with ADS-B. The aircraft flying within United States and below the transition altitude (18,000 feet) are more likely to install a UAT ADS-B. At present, unmanned aircraft systems (UAS) and autonomous air traffic control (ATC) towers are being integrated into the aviation industry and UAT ADS-B is a basic sensor for both class 1 and class 2 detect-and-avoid (DAA) systems. As a fundamental component of future surveillance systems, the anomalies and vulnerabilities of the ADS-B system need to be identified to enable a fully-utilized airspace with enhanced situational awareness. The data received was archived in GDL-90 format, which was parsed into readable data. The anomaly detection of ADS-B messages was based on the FAA ADS-B performance assessment report. The data investigation revealed ADS-B message suffered from different anomalies including dropout, missing payload, data jump, low confidence data, and altitude discrepancy. Among those studied, the most severe was dropout and 32.49% of messages suffered from this anomaly. Dropout is an incident where ADS-B failed to update within a specified rate. Considering the potential danger being imposed, an in-depth analysis was carried out to characterize message dropout. Three flight parameters were selected to investigate their effect on dropout. Statistical analysis was carried out and the Friedman Statistical Test identified that altitude affected dropout more than any other flight parameter.

Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

“…Although adequate performance standards were established for various surveillance equipment, to date the impacts of surveillance system failures in terms of separation degradation, specifically in the UAS platform, have not been defined. Moreover, traditional radar and Mode-A/C transponder technologies show inherent deficiencies in different airspace and equipage scenarios especially in the presence of high air traffic densities [25][26][27][28][29][30][31][32][33]. The failure modes for different cooperative sensors such as Mode S, TCAS, ADS-B are well defined and safety assessments have been carried out on the individual surveillance sensor considering functions in the manned aircraft.…”

Assessing the Effect of ADS-B Message Drop-Out in Detect and Avoid of Unmanned Aircraft System Using Monte Carlo Simulation

“…A python module was developed to decode the archived data as defined in RTCA DO 282B [30]. The anomalies revealed in a step-by-step assessment can be divided into five distinct categories namely dropout, missing payload, low confidence data, data jump, and altitude discrepancy [15,16]. A summary of the ADS-B message evaluation is provided in Table 1 Table 1.…”

“…As the onboard computer will make use of surveillance sensor data to trigger alert in the absence of the 'human eye', it is crucial to have robust and efficient surveillance sensors. However, studies have shown that, both the 1090ES and UAT ADS-B are prone to significant message degradations [8][9][10][11][12][13][14][15][16]. One of the drawbacks of ADS-B is message loss or dropout.…”

“…One of the drawbacks of ADS-B is message loss or dropout. In our previous studies, ADS-B has shown significant message loss along with different message anomalies [13][14][15][16]. In this work, the severity and risk associated with ADS-B drop-out in DAA function are investigated utilizing Monte Carlo simulation.…”

“…This analysis resolves how ADS-B message drop-out could affect UAS DAA alerting if used as a single mean of surveillance without any ATC interaction. Different durations of ADS-B update rate that are detected in the data analysis [16] are used as input to the encounter simulations. Section 2 provides a background of ADS-B message drop out and introduces a well clear metric for evaluating self-separation criteria of UAS.…”