<title>Adaptive Learning Networks And Image Processing For Missile Guidance</title>

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.© (1980) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.