Neural Network Models of Air Combat Maneuvering
Abstract
The primary goal of this project was to explore the applicability of artificial neural network (NN) models in the domain of air combat maneuvering (ACM). The work investigated several models: (a) NN models that select ACM on the basis of training with the production rules of a model, Air Combat Expert Simulation (ACES); (b) NN models that mimic the action selections of the Automated Maneuvering Logic (AML) System; (c) NN models that predict the outcome of engagements flown in the Simulator for Air-to-Air Combat (SAAC) given summary measures of various parameters measured during the engagements; and (d) NN models that predict future aircraft control inputs in SAAC engagements given the values of flight parameters at particular points in time. These various models incorporate knowledge about air combat maneuvers and components of maneuvers as well as rudimentary knowledge about maneuver planning and situational awareness. For most of the models, validation tests were conducted using data different from that used in training the models. The authors provide details on each of these efforts as well as a review of the ACES model, a presentation of the basics of NNs, and an overview of a software system developed for the implementation and testing of the NN models. Air combat, Flight simulation, Performance measurement, Air combat maneuvering, Flight simulators, Flight training, Neural networks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 1992
- Accession Number
- ADA254653
Entities
People
- Alan E. Benson
- Roger W. Schvaneveldt
- Timothy E. Goldsmith
- Wayne L. Waag
Organizations
- New Mexico State University