Recently, adaptive approximation techniques have become popular for obtaining parsimonious representations of large classes of signals. These methods include method of frames, matching pursuit, and, most recently, basis pursuit. In this work, high resolution pursuit (HRP) is developed as an alternative to existing function approximation techniques. Existing techniques do not always efficiently yield representations which are sparse and physically interpretable. HRP is an enhanced version of the matching pursuit algorithm and overcomes the shortcomings of the traditional matching pursuit algorithm by emphasizing local fit over global fit at each stage. Further, the HRP algorithm has the same order of complexity as matching pursuit. In this paper, the HRP algorithm is developed and demonstrated on 1D functions. Convergence properties of HRP are also examined. HRP is also suitable for extracting features which may then be used in recognition. 1 Introduction Recently, adaptive approxima...

We consider a class of interpolation algorithms, including the least-squares optimal Yen interpolator, and we derive a closed-form expression for the interpolation error for interpolators of this type. The error depends on the eigenvalue distribution of a matrix which is specified for each set of sampling points. The error expression can be used to prove that the Yen interpolator is optimal. The implementation of the Yen algorithm suffers from numerical ill-conditioning, forcing the use of a regularized, approximate solution. We suggest a new, approximate solution, consisting of a sinc-kernel interpolator with specially chosen weighting coefficients. The newly designed sinc-kernel interpolator is compared with the usual sinc interpolator using Jacobian (area) weighting, through numerical simulations. We show that the sinc interpolator with Jacobian weighting works well only when the sampling is nearly uniform. The newly designed sinc-kernel interpolator is shown to perform better than ...

We develop the Time Reversal Adaptive Interference Canceler (TRAIC) time reversal beamformer (TRBF), a new algorithm to detect and locate targets in rich scattering environments. It utilizes time reversal in two stages: (1) Anti-focusing: TRAIC time reverses and then reshapes the clutter backscatter to mitigate the clutter response; (2) Focusing: TRBF time reverses the residual backscatter to focus the radar image on the target. Laboratory experiments with electromagnetic radar data in a highly cluttered environment confirm the superiority of TRAIC-TRBF over conventional direct subtraction beamform imaging.

We describe and demonstrate a hierarchical reconstruction algorithm for use in noisy and limitedangle or sparse-angle tomography. The algorithm estimates an object's mass, center of mass, and convex hull from the available projections, and uses this information, along with fundamental mathematical constraints, to estimate a full set of smoothed projections. The mass and center of mass estimates are made using a least squares estimator derived from the principles of consistency of the Radon transform. The convex hull estimate is produced by first estimating the positions of support lines of the object from each available projection and then estimating the overall convex hull using prior shape information. Estimating the position of two support lines from a single projection is accomplished using a generalized likelihood ratio technique for estimating jumps in linear systems. We show results for simulated objects in a variety of measurement situations and discuss several possible extensi...

This paper also proposed another reconstruction method based on a direct approximation of the Fourier inversion formula using a twodimensional (2-D) trapezoidal rule. In addition, the possibility of reconstruction from a concentric-squares raster was discussed. Numerous simple interpolators have been tried in DF reconstruction with the results compared with CBP [33]. In [34] and [35], the concept of angular bandlimiting was used to interpolate the polar data onto a Cartesian grid. In [36], a DF reconstruction using bilinear interpolation for diffraction tomography provided image quality that was comparable to that produced by the CBP algorithm. Very good reconstruction quality was obtained in [37] and [38] using a spline interpolator, or a hybrid type of spline interpolator. The notion of "gridding" was introduced in [39] as a method of obtaining optimal inversion of Fourier data. An optimal gridding function was proposed, and successful results were obtained when applied to the tomographic reconstruction problem. In [40], several different gridding functions were tried for DF reconstruction, and the performances were compared. In [41, 42], the linogram reconstruction method was proposed as a form of DF reconstruction. The data collection grid in the linogram method is the same as in the concentric-squares sampling scheme. The inversion of the Fourier data in [41, 42] was accomplished by first applying the chirp-z transform in one direction and then computing FFTs in the other direction. In CT, many of these attempts at DF reconstruction have given a poorer result than the CBP algorithm, due to the error incurred in the process of the polar-to-Cartesian interpolation. The attraction of DF reconstruction, however, is that it is thought to require less computation than ...

The main topic of this paper is the formulation and performance evaluation of a novel plot-to-track correlation logic for the A-SMGCS (Advanced Surface Movement Guidance and Control System). The conventional plot-track logic exploits the kinematics information only. This logic might fail when more targets move in close proximity. Currently, high resolution radars - integrated in the A-SMGCS - are able to provide the electromagnetic image of the detected targets. Thus, the idea is to exploit also this target feature to improve the plot-track logic. The performance of the new algorithm is evaluated by Monte Carlo simulation using a realistic scenario corresponding to an airport where two targets move in close proximity on the airport surface.

High Range Resolution #HRR# Moving Target Indication #MTI# is becoming increasingly important for many military and civilian applications such as the detection and classi#cation of moving targets in strong clutter background. Meanwhile, using polarization diversity in radar systems has been shown to result in improved performances as compared to using only a single polarization channel. In this paper, we extract HRR moving target features with polarization diversity in the presence of strong stationary clutter. The problem we consider takes into account arbitrary range migration and arbitrary phase errors, whichmay be induced by unknown target and platform motions as well as atmosphere turbulence and#or system instability. A relaxation-based algorithm is presented for the joint clutter suppression and super resolution target feature extraction and its performance is compared to the Cram#er-Rao Bound #CRB#, the best performance bound an unbiased estimator can achieve. Numerical results ...

• Correct Identification Friend or Foe (IFF) decision on a non-cooperative and mainly hostile target which will be unable to fire upon the illuminating radar due to the great

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