This paper explores the use of wavelets to improve the selection of discriminant features in the target recognition problem using high range resolution (HRR) radar signals in an air to air scenario. We show that there is statistically no difference among four different wavelet families in extracting discriminatory features. Since similar results can be obtained from any of the four wavelet families and wavelets within the families, the simplest wavelet (Haar) should be used. We use the box classifier to select the 128 most salient pseudo range bins and then apply the wavelet transform to this reduced set of bins. We show that by iteratively applying this approach, classifier performance is improved. We call this the iterated wavelet transform. The number of times the feature reduction and transformation can be performed while producing improved classifier performance is small and the transformed features are shown to quickly cause the performance to approach an asymptote.
Attempting to find near-optimal architec tures, ontogenic neural networks develop their own architectures as they train. As part of a project entitled "Ontogenic Neural Networks for the Prediction of Chaotic Time Series," this paper presents findings of a ten- week research period on using the Cascade Correlation ontogenic neural network to extrapolate (predict) a chaotic time series generated from the Mackey-Glass equation. During training the neural network forms a model of the Mackey-Glass equation by observing its behavior. Then the neural network is used to simulate the function in order to extrapolate it, that is, to predict its behavior beyond the space observed by the neural network. Truer, more informative measures of extrapolation accuracy than currently popular measures are presented. The effects of some network parameters on extrapolation accuracy were investigated. Sinusoidal activation functions turned out to be best for our data set. The best range for sigmoidal activation functions was [-1, +1]. Though surprisingly good extrapolations have been obtained, there remain pitfalls. These pitfalls are discussed along with possible methods for avoiding them.
Region of interest (ROI) determination is a first and crucial step performed in an automatic target recognition (ATR) system. The goal of ROI determination is to identify candidate regions that may have potential targets. To be most effective, this initial detection (or focus of attention) stage must reject clutter (noise or countermeasures that provide target like characteristics), while ensuring that regions with true targets are not missed. We present a novel approach to ROI determination in synthetic aperture radar (SAR) images for ATR based on the premise that regions with targets would require a model with more free parameters to smoothly approximate the magnitude of the return. Toward that end, we use a sigmoidal multilayered feed-forward neural network with selected lateral connections between hidden layer neurons to approximate the return in disjoint square patches of the SAR image. This network probably uses as few neurons as possible to produce a desired approximation and thus enables the determination of the number of parameters used in approximating the return in an image patch. Those squares of the image that require a large number of neurons (more free parameters) are then labeled as ROIs. Results obtained with synthetic and real-world SAR images are used to demonstrate the effectiveness of the proposed method. A significant advantage of the proposed method is that it does not require the presence of a training data set, which, given the variability in SAR images and target signatures, is difficult to obtain. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms
The Region of Interest (ROl) detection stage of an Automatic Target Recognition (ATR) System serves the crucial role of identifying candidate regions which may have potential targets. The large variability in clutter (noise or countermeasures which provide target like characteristics) complicate the task of developing accurate ROT determination algorithms.Presented in this paper is a new paradigm for ROT determination based on the premise that disjoint local approximation of the regions of a SAR image can provide discriminatory information for clutter identification. Specifically, regions containing targets are more likely to require complex approximators ( i.e. ones with more free parameters or a higher model order).We show preliminary simulations results with two different approximators (sigmoidal multi-layered neural networks with lateral connections, and radial basis function neural networks with a model selection criterion), both of which attempt to produce a smooth approximation of disjoint local patches of the SAR image with as few parameters as possible. Those patches of the image which require a higher model order are then labeled as ROTs. Our preliminary results show that sigmoidal networks provide a more consistent estimate of the model order than their radial basis function counterparts.
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