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In many Fourier imaging applications, the presence of unaccounted for amplitude or phase errors in the Fourier domain data can lead to a degraded system impulse response and high sidelobes in the image domain. Historically, many methods for data-driven correction of these effects have been proposed, and numerical optimization of nonquadratic, p-norm image quality metrics has recently emerged as a robust solution. This paper presents a tutorial examination of the sources of image sideiobes in Fourier imaging applications, and studies the effectiveness of p-norm regularization algorithms under various experimental conditions. Several observations are made, including comments on robustness to noise and methods for tapered window design and energy-constrained sparse aperture imaging. Image examples are presented as experimental validation.

The effect of motion measurement errors (MMEs) on measured bistatic synthetic aperture radar (SAR) phase history data that has been motion compensated to the scene origin is discussed. By deriving limits on the allowable MMEs to be used as system specifications, it is demonstrated that proper orientation of a bistatic SAR image during image formation process allows application of monostatic SAR autofocus algorithms in postprocessing to mitigate image defocus.

The reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images is discussed. A framework for predicting the performance of multistatic stereo SAR algorithm is also proposed to suggest a metric for use in planning strategic deployment of multistatic assets.

The polar format algorithm (PFA) for spotlight synthetic aperture radar (SAR) is based on a linear approximation for the differential range to a scatterer. We derive a second-order Taylor series approximation of the differential range. We provide a simple and concise derivation of both the far-field linear approximation of the differential range, which forms the basis of the PFA, and the corresponding approximation limits based on the second--order terms of the approximation.

Matched filtering (MF) of phase history data is a mathematically ideal but computationally expensive approach to bistatic synthetic aperture radar (SAR) image formation. Fast backprojection algorithms (BPAs) for image formation have recently been shown to give improved O([N.sup.2][log.sub.2]N) performance. In this paper, an O([N.sup.2][log.sub.2]N) bistatic polar format algorithm (PFA) based on a bistatic far-field assumption is derived. This algorithm is a generalization of the popular PFA for monostatic SAR image formation and is highly amenable to implementation with existing monostatic image formation processors. Limits on the size of an imaged scene, analogous to those in monostatic systems, are derived for the bistatic PFA.

Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice was historically driven by available hardware storage, processing capabilities, and data link capacity. Here, we characterize the value of phase in SAR imagery when estimating target features utilized in ATR algorithms for target discrimination. [ABSTRACT FROM AUTHOR]

In this paper, the distortion and defocus effects due to residual phase errors that are inherent in bistatic polar format algorithm are analyzed using time-domain Taylor series decomposition. A two-dimensional interpolation-based distortion correction method is developed, and accurate scene sizes limits are derived to mitigate the effects of defocus. The phase analysis is conducted with respect to arbitrary geometries, and examples are demonstrated for ideal linear and circular flight trajectories using a point target simulation. [ABSTRACT FROM AUTHOR]

We extend the concept of monostatic aspect diversity for improved automatic target recognition (ATR) to a bistatic synthetic aperture radar (SAR) platform. We derive the probability of error with respect to the number of aspects used for a simple two-target template-matching classification system. The validity of the error prediction is confirmed using simulated bistatic SAR images. Our results demonstrate the ATR benefits of supplementing monostatic SAR images with one or more bistatic SAR images. [ABSTRACT FROM AUTHOR]

The polar format algorithm, while being more efficient than backprojection for synthetic aperture radar image formation, produces images with distortion and defocus. Since these errors are caused by uncompensated phase, a correction may be applied in postprocessing. Recent work utilizes a slow-time Taylor expansion of the differential range to derive the residual quadratic phase error for broadside linear flight paths. In this letter, we extend this approach to squinted linear flight paths and present new distortion and defocus corrections. [ABSTRACT FROM AUTHOR]

Training sets for supervised classification tasks are usually limited in scope and only contain examples of a few classes. In practice, classes that were not seen in training are given labels that are always incorrect. Open set recognition (OSR) algorithms address this issue by providing classifiers with a rejection option for unknown samples. In this work, we introduce a new OSR algorithm and compare its performance to other current approaches for open set image classification. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) is a form of remote sensing where coherent radar echoes transmitted from a moving platform are processed to form an image of a scene, usually on the ground. There are several algorithms that have been developed with varying levels of complexity and accuracy. In applications with large scene size requirements, the choice of image formation algorithm is important. Exact imaging algorithms like the back-projection algorithm (BPA) can form large images without errors, but they are computationally expensive. Another well-known algorithm is the polar format algorithm (PFA), which is significantly faster than BPA, but it uses approximations that cause image errors in large scenes. In this paper, we evaluate the scene size limitations of the PFA in terms of image defocus. This is caused by residual quadratic phase errors that arise due to approximations in the algorithm. We derive this residual quadratic phase error using a Taylor series expansion in the slow time dimension. Then, we derive simplified expressions for image defocus for two flight paths: circular and linear. We also use the Taylor series expansion to derive accurate corrections for image distortion caused by PFA. These distortion corrections are used in conjunction with the residual quadratic phase errors to derive accurate scene size limitations that are notably different from the circular regions of focus determined in earlier works. [ABSTRACT FROM AUTHOR]

The polar format algorithm (PFA) for synthetic aperture radar (SAR) image formation utilizes a first-order Taylor approximation of the differential range to improve computational efficiency, leading to image distortion and defocus. Limiting scene size by bounding the second-order Taylor series terms can restrict the impact of these errors, or alternatively, one may correct for these errors through spatially-variant filtering in post processing. In this letter we analyze the increase in allowable scene size that is realized through such processing. [ABSTRACT FROM AUTHOR]

Recently, the use of micro-Doppler radar signatures for target classification has become an area of focus, in particular for the case of dynamic targets where many components are interacting over time. To fully exploit the signature information, individual scattering centers may be automatically extracted and associated over the full target observation. The availability of ultrafine radar range resolution, or micro-range resolution, aids this process immensely. This paper proposes one such algorithm. The proposed method uses the well-known nonlinear least squares (NLS) and expectation-maximization (EM) algorithms. As shown, leveraging fine range and Doppler resolution allows human signatures to be decomposed into the responses of constituent body parts. The algorithm is experimentally validated against a number of measured human-radar data sets. [ABSTRACT FROM AUTHOR]

Power law spectrums are frequently used to model complex natural processes. Estimation of power law behavior can be severely hampered by temporal gaps in measured data, which can occur frequently for data sets spanning many years. The author presents an algorithm for iterative gap filling through the use of an autoregressive model. This technique is validated with a simulation study, and its benefits are demonstrated through application to measured data sets. [ABSTRACT FROM PUBLISHER]

This paper develops three-dimensional (3D), bistatic parametric models that describe canonical radar scattering responses of several geometric objects. These models find use in inverse scattering-based processing of high-frequency radar returns. Canonical feature models are useful for extracting geometry from synthetic-aperture radar (SAR) scattering measurements and as feature primitives for automatic target recognition (ATR) and scene visualization. Previous work has considered monostatic feature models for two-dimensional (2D) radar processing; we extend this work to consider bistatic and 3D radar apertures. In the work presented here, we generalize geometric theory of diffraction (GTD) solutions for several scattering mechanisms in a plane. Products of these planar mechanisms in azimuth and elevation are used to produce 3D bistatic scattering models for six canonical shapes: a rectangular plate, dihedral, trihedral, cylinder, top-hat, and sphere. The derived models are characterized by a small number of parameters, and are shown to agree with results obtained from high-frequency, asymptotic scattering simulations. [ABSTRACT FROM AUTHOR]

The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. Though so-called "tap-leakage" modifications of LMS have been proposed to mitigate this drift, they inevitably introduce parameter bias which degrades mean-squared error performance. In this letter, we propose a novel algorithm which leaks only in the unexcited modes, thus introducing insignificant bias, while still retaining the low computational complexity of LMS. [ABSTRACT FROM AUTHOR]

We consider the design of radar transmit waveforms that are optimal for the purposes of detecting multiple unknown targets in a known interference environment. Specifically, we introduce constraints on the waveform's autocorrelation function, which is of fundamental importance in practice. The incorporation of autocorrelation constraints leaves the problem analytically intractable, and numeric techniques must be employed. We introduce the concept of "waveform optimized performance" in order to provide a framework for suggesting and analyzing various approaches. We show that unlike the solutions to the more relaxed formulations found in the literature, eigenfunction solutions are no longer optimal, and the Neyman-Pearson receiver is no longer the most appropriate choice for many scenarios. Furthermore, for some scenarios, numeric solutions to the autocorrelation-constrained problems can provide signal-to-interference-plus-noise ratio improvements comparable to the solutions of the more relaxed formulations found in the literature. \RDManuscript received March 7, 2009; revised September 7, 2010; released for publication February 21, 2011. [ABSTRACT FROM PUBLISHER]