High Precision ADS-B Based Conflict Alerting System – by Fabrice Kunzi

The actual title of Fabrice Kunzi’s PhD thesis defense presentation is “Development of a High Precision ADS-B Based Conflict Alerting System for Operations in the Airport Environment”.  I had the opportunity to hear Fabrice’s presentation on August 21st and want to say Fabrice and the teams he’s working with at the FAA/SBS, MITRE, Avidyn, and MIT are making a really important contribution to the overall performance of the ADS-B system and safety in the airspace where the likelihood of a mid-air collision is greatest – at relatively low altitudes (where the majority of GA flies) and in relatively close proximity to airports.  This is great news for GA as the algorithms developed through this effort become available in ‘affordable’ ADS-B components for general aviation.

You can view the full presentation here: PhD Thesis Defense – Fabrice Kunzi

And Fabrice’s completd thesis is available here: Phd Thesis (final) – Fabrice Kunzi

Abstract:

Development of a High Precision ADS-B Based Conflict Alerting System for Operations in the Airport Environment

The introduction of Automatic Dependent Surveillance – Broadcast (ADS-B) as the future source of aircraft surveillance worldwide provides an opportunity to introduce high-precision airborne conflict alerting systems for operations in high-density traffic environments. Current alerting systems have been very successful at preventing mid-air collisions in the en-route environment but are of limited benefit near airports where most mid-air collisions occur (59%). Introducing an ADS-B enabled conflict alerting system also generates an incentive for general aviation users to voluntarily equip with ADS-B avionics.The work presented in this thesis describes the process followed to develop an ADS-B enabled, high-precision conflict alerting system that is to serve as the basis for the certification standard that will guide future implementations of such systems. The work was conducted as part of the larger development effort of the Traffic Situation Awareness with Alerting (TSAA) ADS-B application.As a first step, a set of high-level system requirements was identified based on a stakeholder analysis and review of 10 years of historical mid-air collisions. Based on the system requirements, an alerting algorithm was developed that maximizes precedent from currently existing systems, but takes advantage of the improved state information available via ADS-B. The distinguishing factors of the algorithm are its use of a constant turn rate trajectory prediction and the consideration of the current and predicted encounter geometry in the alerting decision.Next, a method to tune the performance of the algorithm was developed and demonstrated. The method applies the Latin hypercube sampling approach to generate a large set of different algorithm implementations, which were then evaluated by simulating the alerting performance on a representative data set of airborne encounters. Lastly, the method also introduced an approach to evaluate and visualize the five-dimensional performance space defined by the five performance metrics of interest for TSAA.

Given the tuned algorithm, a holistic flight test program was conducted. An in-depth analysis of the algorithm performance during the flight test and a comparison to expected performance was performed and is summarized. Given the insights from the tuning and the flight test, additional alerting logic was introduced to the basic algorithm, which significantly improved overall alerting performance. The performance of the sample algorithm was shown to improve the nuisance alert rate by a factor of 5 and reduce the total number of alerts by a factor of 4 over alerting systems currently in use.

 
Fabrice Kunzi
PhD Candidate
International Center for Air Transportation (ICAT)
Massachusetts Institute of Technology, 33-115
(617) 715-4546 | kunzi@mit.edu
 

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