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Proceedings Paper

Adaptive Learning Networks And Image Processing For Missile Guidance
Author(s): Francis J. Cook; Joseph N. Craig
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Paper Abstract

An identification procedure for a generalized nonanalytic, nonlinear autoregressive-moving average process model (GENRA) is introduced. A new set of polynomials representing this model is proposed. When these polynomials are memoryless and analytic, they reduce to the Kolmogorov-Gabor representation of a system. Otherwise, they admit noninteger exponents into an analytic background to provide representations of processes containing singular transformation derivatives. Such noninteger exponents have been observed in the radiation embrittlement of materials, missile nose cone erosion, and thermodynamic phase transitions. The analytic, moving average part of the model is applied separately to the development of advanced optimal missile guidance laws and to image target recognition and aim-point selection. The synthesis that closes the guidance loop to an image terminal homing seeker is under current investigation. An Adaptive Learning Network (ALN) approach to the implementation of GENRA process models is discussed. Two ALN implementation techniques are reviewed: cross validation (decision regularization) and Akaike's Information Criterion. The latter technique was employed to demonstrate the feasibility of using ALN methods to provide passive implementation of modern optimal guidance laws. An ALN implementation of modified proportional navigation demonstrated excellent performance in a six-degree-of-freedom simulation. Results are presented.

Paper Details

Date Published: 23 December 1980
PDF: 10 pages
Proc. SPIE 0238, Image Processing for Missile Guidance, (23 December 1980); doi: 10.1117/12.959158
Show Author Affiliations
Francis J. Cook, Westgate Research Park (United States)
Joseph N. Craig, Westgate Research Park (United States)

Published in SPIE Proceedings Vol. 0238:
Image Processing for Missile Guidance
Thomas F. Wiener, Editor(s)

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