We propose a fast algorithm for far-field SAR imaging based on a new fast back-projection algorithm developed for tomography. We also modify the algorithm for the near-field scenario. The fast back-projection algorithm for SAR has computational complexity O(N²log N). Compared to traditional FFT-based methods, our new algorithm has potential advantages: the new algorithm does not need frequency-domain interpolation, which becomes complex for the wide-angle case; the new approach is applicable to the near-field scenario, taking into account wavefront curvature; and the back-projection algorithm can be easily adapted to parallel computing architectures. For some scenarios of interest, the computational cost of the new backprojection approach is similar to or less than that for FFT-based algorithms.

V. CONCLUSION The root structure of median roots is analyzed by applying three different appending strategies. In this correspondence, we have shown that these three appending strategies have a significant effect on the root structure and the cardinality of root set. We also showed that the appended root signals of the median filter under the circular strategy either consist of constant neighborhoods only or consist of nonconstant neighborhoods only. It is impossible to have a median root under the circular strategy consisting of some constant neighborhoods and some nonconstant neighborhoods.

Abstract not found

The problem of automatic target recognition (ATR) can stated be as the problem of inferring, from the output of one or more sensors directed at a scene, the classes to which objects in the scene belong and the properties of those objects such as sub-class, pose, and states of articulation. We consider the specific problem of ATR based upon synthetic aperture radar (SAR) imagery, though the principles employed are applicable in the wider context of object recognition. Approaches to automated recognition are developed in the context of a communication-based model. The recognition system is viewed as a recipient of information from two sources: a scene containing the object to be recognized and a database characterizing the objects to be recognized. The overall accuracy of the system is dependent upon the properties of the scene and sensor, the accuracy of the imaging model on which the system is based, and the accuracy of approximations made for the purpose of system implementation. These last two items have a direct impact on the computational resource requirements of a recognition system. The accuracy of a system is thus directly related to the available resources, such as the number of processor cycles used, mass storage requirements, network bandwidth utilization, elapsed time, etc. This relationship can be characterized by an accuracy-consumption curve which is useful for comparing alternate approaches to recognition and for exploring the space of system design possibilities. A statistical hypothesis testing approach is followed and several variants of four probab...

We present an evaluation of the impact of a recently proposed synthetic aperture radar (SAR) imaging technique on feature enhancement and automatic target recognition (ATR) performance. This image formation technique is based on non-quadratic optimization, and the images it produces appear to exhibit enhanced features. In the first part of this paper, we quantify such feature enhancement through a number of criteria. The findings of our analysis indicate that the new feature-enhanced SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared to conventional SAR images. In the second part of this paper, we provide an ATRbased evaluation. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, with three different classifiers. The first classifier is template-based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that the new feature-enhanced SAR imaging method can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.

We present a new non-iterative approach to synthetic aperture radar (SAR) autofocus, termed the MultiChannel Autofocus (MCA) algorithm. The key in the approach is to exploit the multichannel redundancy of the defocusing operation to create a linear subspace, where the unknown perfectly-focused image resides, expressed in terms of a known basis formed from the given defocused image. A unique solution for the perfectlyfocused image is then directly determined through a linear algebraic formulation by invoking an additional image support condition. The MCA approach is found to be computationally efficient and robust, and does not require prior assumptions about the SAR scene used in existing methods. In addition, the vector-space formulation of MCA allows sharpness metric optimization to be easily incorporated within the restoration framework as a regularization term. We present experimental results characterizing the performance of MCA in comparison with conventional autofocus methods, and discuss the practical implementation of the technique.

This dissertation addresses data-driven image restoration for computed imaging systems. The work is focused on problems in two imaging modalities: the autofocus problem in synthetic aperture radar (SAR), and the problem of estimating coil sensitivities in parallel magnetic resonance imaging (PMRI). A common thread in both problems is their inherent multichannel nature, i.e., both exhibit special structure due to the redundancy provided by multiple signal measurements. By explicitly exploiting the multichannel structure, novel algorithms are developed offering improved restoration performance. We first present a theoretical study providing more insight into metric-based SAR autofocus techniques. Our analytical results show how metric-based methods implicitly rely on the multichannel defocusing model of SAR autofocus to form well-focused restorations. Utilizing the multichannel structure of the SAR autofocus problem explicitly, we develop a new noniterative restoration approach termed the MuliChannel Autofocus (MCA) algorithm. In this approach, the focused image is directly recovered using a linear algebraic formulation. Experimental results using actual and simulated SAR data demonstrate that MCA provides superior performance in comparison with existing autofocus methods. Lastly, we develop a new subspace-based approach for estimating receiver coil sensitivity functions used in PMRI reconstruction. Our approach does not rely on sum-of-squares assumptions used in previous PMRI techniques, thus avoiding potential problems such as poor image contrast and aliasing artifacts.