We investigate a type of artificial neural network which has been called a high order network for application to the millimeter wave (MMW) radar stationary target classification problem. The high order network like the multilayer perceptron provides a minimum mean square error (MMSE) estimate of the optimal discriminant, however, the high order network has the advantage of ease of training. This network can be trained via iterative gradient descent and also by a closed form one-pass solution. Using real beam Ka-band radar field data, we compare the classification performance of the high order network with that of a gaussian classifier for several conditions. We found that the high order network performance is improved over the gaussian classifier and further, we obtained very attractive results with the one-pass solution.
In this study we consider a family of polynomial classifiers and compare the performance of these classifiers to the Mahalanobis Distance classifier and to two types of artificial neural networks- -multilayer perceptrons and high-order neural networks. The well-known Mahalanobis Distance classifier is based on the assumption that the underlying probability distributions are Gaussian. The neural network classifiers and polynomial classifiers make no assumptions regarding underlying distributions. The decision boundaries of the polynomial classifier can be made to be arbitrarily nonlinear corresponding to the degree of the polynomial hence comparable to those of the neural networks. Further we describe both iterative gradient descent and batch procedures by which the polynomial classifiers can be trained. These procedures provide much faster training than that achievable for multilayer perceptrons trained via backpropagation. We demonstrate that the polynomial classifier and high-order neural network can be equated thereby implying that the classification power of the multilayer perceptron can be achieved while retaining the ease of training advantages of the polynomial classifiers. 1.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.