Air traffic systems have long relied on automated short-term conflict prediction algorithms to warn controllers of impending conflicts (losses of separation). The complexity of terminal airspace has proven difficult for such systems, as it often leads to excessive false alerts. Thus, the legacy system, called Conflict Alert, which currently provides shortterm alerts in both en route and terminal airspace, is often inhibited or desensitized in areas where frequent false alerts occur, even though the alerts are provided only when an aircraft is in dangerous proximity of other aircraft. This research investigates how a minimal level of flight-intent information may be used to improve short-term conflict detection in terminal airspace such that it can be used by the controller to maintain legal aircraft separation. The flight-intent information includes a site-specific nominal arrival route and inferred altitude clearances in addition to the flight plan that includes the area-navigation departure route. A new tactical conflict detection algorithm is proposed, which uses a single analytic trajectory, determined from the flight intent and the current state information of the aircraft, and includes a complex set of current, dynamic separation standards for terminal airspace. The new algorithm is compared with an algorithm that models a known en route algorithm and another algorithm that models Conflict Alert. This is done by analysis of false-alert rate and alert lead time with the use of recent real-world data of arrival and departure operations and a large set of operational error cases from the Dallas/ Fort Worth terminal radar approach control. The new algorithm yielded a false-alert rate of two per hour and an average alert lead time of 38 s.

A method for determining the severity of impending losses of separation (LOSs) is proposed. It is based on the FAA (Federal Aviation Administration) separation conformance category for classification of operational errors. A recently proposed short-term conflict detection algorithm for terminal airspace is enhanced with this severity concept. The alerts from the resulting algorithm are compared with the Conflict Alert (CA) currently in the field, which is a legacy system for automated short-term conflict detection. Three complementary sets of aircraft track data are employed. The first set is real-world data with documented LOSs due to operational errors. It allows determination of average alert lead time while providing regression testing of the algorithm. The second set is realistic data from human-in-the-loop experiments with no visual approaches allowed and with known intervention from controllers or pilots available. As a result more objective determination of false alert rate is possible. The third set is real-world data with unknown mixed operations of Instrument Landing System (ILS) and visual approaches but with CA alerting data available from the FAA. The comparison with CA indicates that the algorithm produces a similar total number of alerts but with a much larger safety buffer and a much lower false alert rate. The study also suggests that a high-severity conflict prediction option may be used for aircraft performing visual approaches to satisfy the controller's moral responsibility for those aircraft.

A tactical separation assurance prototype system is evaluated for its fitness to support the Standard Terminal Automation Replacement System (STARS) in a complex terminal airspace environment that includes a mix of visual-and instrument-approach aircraft, Mode C intruders, and limited trajectory-intent data. Fast-time simulation experiments using air traffic data from human-in-the-loop simulations and live Terminal Radar Control (TRACON) operations featuring a mix of visual and instrument approaches and Mode C intruders are performed to assess the performance and benefits of the system in a near-term national airspace system (NAS). It is found that nuisance alerts attributable to aircraft on visual approach are eliminated with a high-severity alerting option. With a normal lowseverity alerting option, Mode C intruder alerts are reduced more than 50% as compared to the Conflict Alert system, a legacy function in STARS. The trajectory intent information that is most effective in reducing false alerts is identified and found to be available in STARS or easily adapted from existing NAS automation.

No abstract

No abstract