Leveraging Aircraft Transponder Signals for Measuring Aircraft Fleet Mix at Non-Towered Airports

The general aviation sector has contributed significantly to the overall growth of the aviation industry. Fleet mix and operations information is important for the analysis of both the safety and the environmental and economic impact of general aviation activity. Factual data on the impact of environment and safety is particularly important, given the scarcity of airport improvement funds and the strong stakeholder engagement that often occurs when airport investments are evaluated. The research presented herein evaluates Mode S short and extended squitter data collected from three general aviation airports over a one-month period. This article demonstrates that by utilizing the unique ICAO code present in Mode S records in the United States, ICAO identification can be used as a primary key for retrieving information from publicly available databases, permitting the determination of aircraft type and engine models. An aircraft operation type with the corresponding fleet mix information can thus be estimated at non-towered airports. The techniques reported here are also useful in monitoring the increase in aircraft compliant with the FAA’s January 1, 2020 mandate for ADS-B out to operate in large portions of the national airspace system. For the three Indiana airports studied, the proportion of ADS-B transponder messages received ranged from approximately 29% at the Terre Haute Regional Airport (KHUF) to 79% at the Purdue University Airport (KLAF)

Artificial intelligence for helicopter safety: Head pose estimation in the cockpit

The recent impact of deep learning algorithms and their major breakthroughs on various aspects of our lives has led to the idea to investigate the application of these algorithms in different problem spaces. One of the novel areas of investigation is the aviation and air traffic control domain; as it offers a prime opportunity to enhance safety within the aviation community. Of particular importance to this community is improving the safety of rotorcraft operations, as this segment of the aviation industry is subject to a higher fatal accident rate than other segments of the industry. The improvement of safety for rotorcraft also directly improves the safety and efficiency of air traffic control, since rotorcraft operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants such as unmanned aircraft systems, urban air mobility, etc. The novel method for improving rotorcraft safety, and the main topic of this research, is to create an algorithm that determines the head position of helicopter pilots and copilots through automatic post-processing of onboard flight video data. This information can then be used to aid in incident/crash analysis as well as future vision systems research. Both a classical computer vision technique and a deep learning approach were taken to provide possible solutions to this problem. Both solutions successfully deal with the issues of excessive cockpit background, extreme head positions, and added noise from the pilot\u27s operational equipment which include helmets, microphones, and sunglasses