Range-Doppler (RD) algorithms are widely used in inverse synthetic aperture radar (ISAR) imaging. In the standard RD algorithm, envelope alignment and autofocus are first applied to transform the original data into equivalent turntable target data, and then FFT is used for the image formation. Usually, the migration through resolution cells (MTRCs) due to target rotation is ignored. With the improvement of resolution or the increase of target size, MTRCs cannot be ignored and must be mitigated. For high speed moving targets, the most widely used 'stop-and-go' data model is violated. In this case, the mitigation of MTRCs becomes even more complicated. In the paper, technical issues associated with high resolution ISAR imaging of high speed moving targets are addressed. A preprocessing algorithm is first proposed to convert the non-coherent raw ISAR data into coherent data, then a keystone formatting algorithm developed for the imaging of slow ground moving targets in SAR is applied to mitigate the MTRCs owing to rotational motion, and, finally, a high resolution time-frequency analysis based range-instantaneous Doppler (RID) algorithm is used to produce the focused ISAR image. Numerical examples are provided to illustrate the performance of the proposed approach.

We present a conceptually simple and computationally efficient algorithm, which is referred to as WRELAX, for the well-known time delay estimation problem. The method is a relaxation-based minimizer of a complicated nonlinear least squares criterion. WRELAX can be applied to detecting and classifying roadway subsurface anomalies by using an ultra-wideband ground-penetrating radar. Numerical and experimental examples are provided to demonstrate the performance of the new algorithm.

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.

APES (Amplitude and Phase EStimationl is an adaptive FIR filtering approach introduced recently for complex spectral estimation. For stationary signals, APES can yield more accurate spectral estimates than the widely used Capon and FFT estimators. The authors present a recursive APES algorithm for time-varying spectral analysis, which is computationaly efficient and only involves FFT and simple matrix operations. The recursive APES algorithm is applied to ISAR (inverse synthetic aperture radar) imaging and feature extraction of targets with complex manoeuvring motion. Both numerical and experimental examples are provided to demonstrate the performance of the proposed method.

Range-Doppler (RD) processing is widely used in conventional inverse synthetic aperture radar (ISAR) imaging. The unwanted translational motion of moving targets is compensated by envelope alignment and autofocus. For existing ISAR imaging algorithms, the scatterers\' migration through resolution cells (MTRC) caused by the rotational motion is usually ignored. With the improvement of resolution or the increase of target size, MTRC cannot be neglected. In this letter, the keystone formatting algorithm developed in SAR is used for the MTRC compensation in ISAR. Before the keystone formatting, coherent processing must be performed on the raw phase history data. An effective approach is proposed for this kind of coherent processing. Numerical examples are provided to demonstrate the performance of the proposed approach.