An inverse synthetic aperture radar (ISAR) can be used to produce high-resolution images of moving targets of interest by utilizing the relative motion between the target and the radar and by transmitting signals with large bandwidth. Most ISAR imaging algorithms are based on the range-Doppler processing, which implies that the Doppler shifts remain constant during the coherent integration time. For maneuvering targets, the Doppler shifts are time varying. In this case, the algorithms will produce blurred images. We present herein an adaptive Capon spectral estimation algorithm for the complex ISAR image formation of maneuvering targets. It is an efficient recursive implementation of the well-known Capon complex spectral estimation algorithm by using FFT and simple matrix operations.

It is demonstrated through simulations that a combination of the vector auto-regressive (VAR) filtering technique and a maximum likelihood (ML) parameter estimation method is an effective means for carrying out clutter suppression and parameter estimation, and being more robust against system mismatches than conventional displaced-phase-centre-antenna (DPCA) processing.

An FFT (fast Fourier transform) based parametric algorithm. rcferred to as MCCLEAN. is devised tor cross-range phase error compensation in SAR (synthetic aperture radar) imaging. It can be used as an independent autofocus approach for the SAR imaging of a large scene. The authors also present a similarly structured algorithm for simultaneous autofocus and super．resolution SAR image formation of a small scene or small region of interest (ROl) in a large scene. Examples are provided to illustrate the performance of the proposed algorithms

A new automatic target recognition (ATR) scheme based on oncdimensioaal high range resolution profiles is presented, which features joint range alignment and classification This meThod CKI also be used to form mean-or eigen-templates to fianher improve the ATR performance Experimental results are provided to demonstcate the performance of the new algorithm.