We present a model for classification performance estimation for synthetic aperture radar (SAR) automatic target recognition. We adopt a model-based approach, in which classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are compared using a Bayes likeliho...

The Lincoln Laboratory baseline ATR system for synthetic aperture radar (SAR) data applies a superresolution technique known as high definition imaging (HDI) before the input image is passed through the final target classification subsystem. In previous studies, it has been demonstrated that HDI improves target recognition performance significantly. Recently, however, several other viable SAR i...

Compact polarimetric (CP) data exploitation is currently of growing interest considering the new generation of such Synthetic Aperture Radar (SAR) systems. These systems offer target detection and classification capabilities comparable to those of polarimetric SARs (PolSAR) with less stringent requirements. A good example is the RADARSAT Constellation Mission (RCM). In this paper, some characte...

In recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing litera...

Ultra-wideband pulse radar is a promising technology for the imaging sensors of rescue robots operating in disaster scenarios, where optical sensors are not applicable because of thick smog or high-density gas. For the above application, while one promising ultra-wideband radar imaging algorithm for a target with arbitrary motion has already been proposed with a compact observation model, it is...

............................................................................................................... 13 CHAPTER 1 – INTRODUCTION ........................................................................... 15 CHAPTER 2 – AUTOMATIC TARGET RECOGNITION .................................. 22 2.1 Automatic Target Recognition for Monostatic Radar ................................. 22 2.1.1 Pr...

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, ...

This paper describes the design of the automatic target classifier which has been introduced into the AMSTAR Battlefield Surveillance Radar. It discusses the requirements which have driven the design of the classifier, the data which is used to make the classification, the choice of Linear Discriminant Analysis as one of the classification techniques used and the use of Principal Components Ana...

Obtaining classification systems with both good generalization performance and high recognition rates often requires careful study on methods of combining multiple classifiers. In this paper, a Kohonen self-organization neural network based combination method is proposed. The system uses the Kohonen's model to integrate the results of several neural network classifiers. The theoretical analysis...

A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The...