Space-time adaptive processing (STAP) for airborne early warning radar has been a very active area of research since the late 1980's. An airborne rectangular planar array antenna is usually configured into subarrays and then partial adaptive processing is applied to the outputs of these subarrays. In practice, three kinds of errors are often encountered, i.e., the array gain and phase errors existing in each element, the channel gain and hase errors, and the clutter covariance matrix estimation errors due to insufficient secondary data samples. These errors not only degrade the clutter suppression performance, but also cause the adapted array patterns to suffer much distortion (high sidelobes and distorted mainbeams), which may result in the rise of falsealarm probability and make the adaptive monopulse tracking and sidelobe blankering more difficult. In this paper, the causes of the above three kinds of errors to array pattern distortion are discussed and a novel quadratic soft constraint factored approach is proposed to precisely control the peak sidelobe level of adapted patterns. The soft constraint factor can be determined explicitly according to the peak sidelobe level desired and the known or desired tolerant error standard deviations. Numerical results obtained by using high-fidelity simulated airborne radar clutter data are provided to illustrate the performance of the proposed approach. Although the method is presented for STAP, it can be directly applied to the conventional adaptive beamforming for rectangular planar arrays used to suppress jammers.

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.

In modern radar, sonar and wireless communication systems digital beamforming technology based on sensor arrays is widely used to suppress unwanted interfering signals and detect interested target signals．Beam pattern synthesis for arbitrary arrays and robust beamforming for imperfect arrays are two crucial technical issues associated with digital beamforming．In a complex volumetric underwater acoustic transducer array, due tostructural shadowing and scattering. the practical beam pattern significantly deviates from its designed pattern, which would greatly degrade the detection and direction. finding performance of the sonar system. An efficient method is proposed to design beam patterns for arbitrary arrays and is applied to robust beamforming for this sonar system. Sea experimental results show that the proposed robust beamforming method achieves a much lower sidelobe level and a much higher direction. finding accuracy than the conventional beamforming technique.

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

Adaptive beamforming techniques are now widely used to reject interference (jammer/clutter) signals in radar, communication, and sonar applications. In adaptive arrays using the sample matrix inversion (SMI) algorithm, inadequate estimation of the covariance matrix results in adaptive antenna patterns with high sideiobes and distorted mainbeams. In this paper, a method is proposed to precisely control the peak (rather than average) sidelobe level of adapted array patterns. The proposed method is also generalized to adaptive array antennas with moderate bandwidth and large random amplitude and phase errors. Theoretical analysis and simulation results are provided to illustrate the performance of the method proposed.