An airborne collision detection and alerting system for general aviation

Abstract

Aviation has been a growing industry due to being a key contribution in providing a rapid worldwide transportation network. In view of this, more people are aspiring to become airline pilots in response to such demand. This leads to an increase in training flights by virtue of the training required. With the already busy airspace and the increasing training flights, more mid-air collisions are prone to happen. Airborne Collision Avoidance Systems (ACAS) already exist and proven to be successful in hindering a mid-air collision from happening. Regrettably, ACAS systems are primarily designed to operate on large commercial aircraft with no intent to be installed on light training aircraft. This raises the need of having an ACAS system equally available to light aircraft. To achieve this, the proposed system will operate using the already available technology that of Automatic Dependent Surveillance – Broadcast (ADS-B) in conjunction with a number of algorithm programs. These algorithms will deduce if any surrounding traffic are a potential threat. This dissertation discusses some research into radar technology and its mode of operation, the extraction and decoding of ADS-B data from Mode-S extended squitters, hardware and software selection as well as designing algorithms for evaluating the decoded ADS-B data. The software was improved over multiple consecutive tests to improve the software’s capability. For the purpose of practicing situational awareness as well as being able to advise of any potential conflict, two algorithms were designed to operate in parallel. These will evaluate traffic based on the time to closest approach (CPA) or lateral distance between both aircraft independently. A number of scenarios were tested in examining different portions of the algorithm. All results were tabulated and details about the occurrences are put forward. Real-life testing was conducting on actual aircraft, testing the operation of the whole system from capturing Mode-S extended squitters to advising the user with any alerts. Tests conducted both in a simulation environment and using real-life ADS-B data were successful. This provides confidence in the correct operation of the algorithms and indicates that the system has the potential to assist general aviation pilots in identifying traffic threats.