We consider estimating time delays and amplitudes (real-or complex-valued) from the superposition of very closely spaced signals with known shapes. The well—known high resolution MODE (Method Of Direction Estimation) algorithm, which was originally proposed for angle estimation in array processing, is modified and used with our efficient time delay estimation method WRELAX (Weighted Fourier transformand RELAXation based) algorithm to deal with this problem. The proposed method is referred to as MODE-WRELAX. MODE-WRELAX provides better accuracy than MODE and higher resolution than WRELAX. Moreove it can be used for both complex-and real-valued signals (including those with highly oscillatory correlation functions). Numerical results show that the MODE-WRELAX estimates can approach the corresponding Cram6r-Rao bound. Efficient implementation of the algorithm is also discussed.

High range resolution (HRR) moving target indicator (MTI) is becoming increasingly important for many military and dvilian applications such as the detection and dassification of moving targets in strong clutter background. We consider the problem of extracting the HRR features of moving targets with very dosely spaced scatterers in the presence of strong stationary clutter, where the range migration and Vopplel\"frequency are taken into account. A relaxation-based algorithm，which is robust and computationally simple, is propOsed to deal with the above problem. Numerical results have shown that the proposed algorithm exhibits super resolution and excellent estimation performance.

A new algorithm, referred to as the SPAR (Semiparametric) algorithm, is presented herein for target feature extraction and complex image formation via synthetic aperture radar (SAR). The algorithm is based on a fiexible data model that models each target scatterer as a two-dimensional (2-D) complex sinusoid with arbitrary unknown amplitude and constant phase in cross-range and with constant amplitude and phase in range. By attempting to deal with one corner reflector, such as one dihedral or trihedral, at a time, the algorithm can be used to effectively mitigate the artifacts in the SAR images due to the flexible data model. Another advantage of SPAR is that it can be used to obtain initial conditions needed by other parametric target feature extraction methods to reduce the total amount of computations needed. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm.

A robust autofoeus approach, referred to as AUTOCLEAN (AUTOfoCUS via CLEAN), is proposed for the motion compensation in ISAR (inverse synthetic aperture radar) imaging of moving targets. It is aparametric algorithm based on a very flexible data model which takes into account arbitrary range migration and arbitraryphase errors acrossthe synthetic aperture thatmaybeinduced by unwanted radialmotion of the target as well as propagation or system instabifity. AUTOCLEAN can bedassifiedus amultiple scatterer algorithm (MSA), but itdiffers considerablyfrom 0tller existing MSAs in several aspects.1) Dominant scatterers are selected automatically in the two-dimensional (2-D) image domain;2) scatterers may not be well isolated or verydominant;3) phase and RCS (radar Cross section) informationfrom each seiected scattererare eombined inan optimalway;4) the troublesome phase unwrapping step is avoided. AUTOCLEAN is computationally efficient and involves only a sequence of FFTs (fast Fourier transforms). Another goodfeature associated with AUTOCLEAN is that its performance can beprogressively improved by assumingalarger number of dominant scatterers for the target. Numerical and experimental results hare shown that AUTOCLEAN is a very robust autofoCus tool for ISAR imaging.

To detect buried landmines, Planning Systems Incorporated (PSI) has developed a Ground Penetrat-ing Synthetic Aperture Radar (GPSAR) system. GPSAR can be used to generate three-dimensional images of mines. It has been shown that the SAR processing in the PSI GPSAR system can greatly improve the image quality and hence the mine (especially plastic mine) detection performance. Since the electromagnetic wave propagation velocity in the soil depends on many factors, inchlding the soil texture, bulk density, as well as water content, and it changes from one location to another, velocity uncertainty is inevitable. It is well-known that for the conventional airborne/spaceborne SAR processing, velocity uncertainty may result in phase errors that will severely blur the SAR image. However, we have observed from experimental examples that the PSI GPSAR system is very robust against the velocity uncertainty. More specifically, under mild conditions velocity uncertainty does not defoeus the image but only scales the image along the depth dimension, and hence will not affcct the mine detection performance. Theoretical analysis is provided to explain this observation. Although our discussion is based on the PSI GPSAR system, it applies to other GPR based landmine detection systems as well.