Flight Segment Identification as a Basis for
Pilot Advisory Systems
Dr. Wallace E. Kelly III*
Blue Rock Research and Development, Inc., Asheboro, North Carolina, 27502
Dr. John H. Painter.
Altair Corporation, College Station, Texas,
Flight Segment Identification (FSI) is the process of monitoring aircraft state variables and flight events to identify in real-time the phase of flight or operational procedure of an aircraft. It is a dynamic classification problem in which the state space is highly dimensional and the boundaries between the various flight phases are not crisply defined. Examples of flight segments include “enroute cruise,” “holding,” and “initial approach.” We explain the role of Flight Segment Identification (FSI) in building pilot advisory systems, including a new distinction that we propose between state-based FSI and procedural FSI. State-based FSI uses the aircraft state variables to infer the flight operation currently being executed by the pilot. Procedural FSI tracks the flight operation that the pilot should be executing now – based on events and flight rules that are largely outside the control of the pilot. We present one approach to performing Flight Segment Identification based on fuzzy sets and how we applied this solution to the NASA High Volume Operations concept. Finally, we discuss our results and conclusions from recent flight tests of a Flight Segment Identifier.
Nomenclature
mA(x) = the fuzzy degree of membership of data x in fuzzy set A.
li = a prototype point, to define a hypertrapezoidal fuzzy set i.
s = the crispness factor of a hypertrapezoidal partitioning.
d(x, y) = the Euclidean distance between data points x and y.
ri|j(x) = an intermediate distance value used for calculating hypertrapezoidal fuzzy membership.
I. Introduction
vionics software with artificial intelligence could assist pilots in following flight procedures. There are several Amotivations for doing this. The first is that the amount of available information in future cockpits will continue
to grow and the complexity of the avionics to manage that information will likewise increase. AI technologies can help monitor and prioritize the information flow in the cockpit. Secondly, “smarter” software in the cockpit would simplify the task of flight for newer and less experienced pilots. NASA and other industry leaders foresee a new class of “personal air vehicles,” which brings aviation to more people. Making those vehicles easier to operate is an important research goal for the industry. The goal was recently described by NASA as “simplify the operation of small aircraft such that the specialized skills, knowledge, and associated training are reduced to levels comparable to operating an automobile or boat.”1 Another motivation, which is the primary focus of this paper, is that avionics with Pilot Advisor functionality can enable new flight procedures that make our National Airspace System operate more efficiently.
It is worth clarifying what is meant by “avionics software with artificial intelligence.” The goal is to engineer onboard computer systems that assist the pilot much like an instructor pilot might monitor and advise a student pilot.
* President, Blue Rock Research, 4998 Old NC-49, Asheboro, NC, 27205-0117, Member, AIAA.
. President, Altair Corporation, P.O. Box 10046, College Station, TX, 77842-0046, Member, AIAA.
Completely replicating that kind of expertise would be quite a challenge. The industry is no where near that level of capability. But we are making progress.
Besides the in-flight benefits, there is also the potential for a training aid during simulation. A “verbose mode” of a Pilot Advisor can be used to pause a flight simulator when a student makes a significant mistake. Either a printed commentary may be projected on the display or synthetic voice may give the required training commentary. After digesting the commentary, the student may then un-pause the simulator and proceed with the flight. This training mode in a Pilot Advisor would be extremely useful in teaching such procedures as Instrument Approaches (ILS, GPS, etc.)
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