Andrew R. Webb

A Bayesian approach to simultaneous autofocus and superresolution

This paper presents a numerical Bayesian approach to the autofocus and super-resolution of targets in radar imagery. An ill-posed inverse problem is studied in which the known linear imaging operator is subject to an unknown degree of distortion (defocusing). The goal is simultaneously to reconstruct a high-resolution representation of a target based on noisy lower resolution image measurements and to estimate the degree of defocus. We present a Markov chain Monte Carlo algorithm for parameter estimation, illustrate the approach on an explanatory example and compare our technique with a maximum likelihood approach. Given a model for the sensor measurement process, this technique may be applied to any type of radar image such as those produced by a synthetic aperture radar (SAR), inverse SAR (ISAR) or a real beam imaging radar. The proposed approach fits into a larger set of procedures aiming to exploit targeting information from different radar sensors.

Bayesian Networks for Incorporation of Contextual Information in Target Recognition Systems

In this paper we examine probabilistically the incorporation of contextual information into an automatic target recognition system. In particular, we attempt to recognise multiple military targets, given measurements on the targets, knowledge of the likely groups of targets and measurements on the terrain in which the targets lie. This allows us to take into account such factors as clustering of targets, preference to hiding next to cover at the extremities of fields and ability to traverse different types of terrain. Bayesian networks are used to formulate the uncertain causal relationships underlying such a scheme. Results for a simulated example, when compared to the use of independent Bayesian classifiers, show improved performance in recognising both groups of targets and individual targets.

Classifier Design for Population and Sensor Drift

The basic assumption in classifier design is that the distribution from which the design sample is selected is the same as the distribution from which future objects will arise: i.e., that the training set is representative of the operating conditions. In many applications, this assumption is not valid. In this paper, we discuss sources of variation and possible approaches to handling it. We then focus on a problem in radar target recognition in which the operating sensor differs from the sensor used to gather the training data. For situations where the physical and processing models for the sensors are known, a solution based on Bayesian image restoration is proposed.

A Kernel Approach to Metric Multidimensional Scaling

The solution for the parameters of a nonlinear mapping in a metricm ultidimensional scaling by transformation, in which a stress criterion is optimised, satisfies a nonlinear eigenvector equation, which may be solved iteratively. This can be cast in a kernel-based framework in which the configuration of training samples in the transformation space may be found iteratively by successive linear projections, without the need for gradient calculations. A new data sample can be projected using knowledge of the kernel and the final configuration of data points.

Nonlinear feature extraction for MMW image classification: a supervised approach

The specular nature of Radar imagery causes problems for ATR as small changes to the configuration of targets can result in significant changes to the resulting target signature. This adds to the challenge of constructing a classifier that is both robust to changes in target configuration and capable of generalizing to previously unseen targets. Here, we describe the application of a nonlinear Radial Basis Function (RBF) transformation to perform feature extraction on millimeter-wave (MMW) imagery of target vehicles. The features extracted were used as inputs to a nearest-neighbor classifier to obtain measures of classification performance. The training of the feature extraction stage was by way of a loss function that quantified the amount of data structure preserved in the transformation to feature space. In this paper we describe a supervised extension to the loss function and explore the value of using the supervised training process over the unsupervised approach and compare with results obtained using a supervised linear technique (Linear Discriminant Analysis --- LDA). The data used were Inverse Synthetic Aperture Radar (ISAR) images of armored vehicles gathered at 94GHz and were categorized as Armored Personnel Carrier, Main Battle Tank or Air Defense Unit. We find that the form of supervision used in this work is an advantage when the number of features used for classification is low, with the conclusion that the supervision allows information useful for discrimination between classes to be distilled into fewer features. When only one example of each class is used for training purposes, the LDA results are comparable to the RBF results. However, when an additional example is added per class, the RBF results are significantly better than those from LDA. Thus, the RBF technique seems better able to make use of the extra knowledge available to the system about variability between different examples of the same class.