Title :
Optimum classification of non-Gaussian processes using neural networks
Author :
Blacknell, D. ; White, R.G.
Author_Institution :
Defence Res. Agency, Great Malvern, UK
fDate :
2/1/1994 12:00:00 AM
Abstract :
A prerequisite for target detection in synthetic aperture radar and moving target imaging radars is an ability to classify background clutter in an optimal manner. Such radar clutter can frequently be modelled as a correlated nonGaussian process with, for example, Weibull or K statistics. Maximum likelihood (ML) provides an optimum classification scheme but cannot always be formulated when correlations are present. In such circumstances, nonlinear, adaptive filters are required which can learn to classify the clutter types: a role to which neural networks are particularly suited. The authors investigate how closely neural networks can approach optimum classification. To this end, a factorisation technique is presented which aids convergence to the best possible solution obtainable from the training data. The performances of factorised networks are compared with the ML performance and the performances of various intuitive and approximate classification schemes when applied to uncorrelated K distributed images. Furthermore, preliminary results are presented for the classification of correlated processes. It is seen that factorised neural networks can produce an accurate numerical approximation to the ML solution and will thus be of great benefit in radar clutter classification
Keywords :
feedforward neural nets; image recognition; maximum likelihood estimation; radar clutter; signal detection; synthetic aperture radar; Weibull statistics; approximate classification; background clutter classification; correlated nonGaussian process; factorisation technique; factorised neural networks; intuitive classification; maximum likelihood performance; moving target imaging radars; multilayer perception; neural networks; nonlinear adaptive filters; numerical approximation; optimum classification; radar clutter; synthetic aperture radar; target detection; training data; uncorrelated K distributed images;
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
DOI :
10.1049/ip-vis:19949708