Search

A beamspace adaptive planar array for broadband beamforming is proposed based on the frequency invariant beamforming technique. A detailed analysis is provided for both the linear arrays and the planar arrays, where two conditions are imposed: the beams should exhibit a good frequency invariant property and be linearly independent. These two conditions are not independent and there is a tradeoff between them. To improve the interference cancellation capability of the array, we may need to sacrifice the frequency invariant property to some degree for more linearly independent beams. A sufficient condition for the linear independence of the beams is derived with a DFT matrix based design proposed. Simulation results verified our analysis and the proposed method. Index Terms--Adaptive arrays, antenna arrays, beam steering, broadband communication.

Currently, the nonadaptive delay-and-sum (DAS) beamformer is extensively used for ultrasound imaging, despite the fact that it has lower resolution and worse interference suppression capability than the adaptive standard Capon beamformer (SCB) if the steering vector corresponding to the signal of interest (SOI) is accurately known. The main problem which restricts the use of SCB, however, is that SCB lacks robustness against steering vector errors that are inevitable in practice. Whenever this happens, the performance of SCB may become worse than that of DAS. Therefore, a robust adaptive beamformer is desirable to maintain the robustness of DAS and adaptivity of SCB. In this paper we consider a recent promising robust Capon beamformer (RCB) for ultrasound imaging. We propose two ways of implementing RCB, one based on time delay and the other based on time reversal. RCB extends SCB by allowing the array steering vector to be within an uncertainty set. Hence, it restores the appeal of SCB including its high resolution and superb interference suppression capabilities, and also retains the attractiveness of DAS including its robustness against steering vector errors. The time-delay-based RCB can tolerate the misalignmcnt of data samples and the time-reversal-based RCB can withstand the uncertainty of the Green's function. Both time-delay-based RCB and time-reversal-based RCB can be efficiently computed at a comparable cost to SCB. The excellent performances of the proposed robust adaptive beamforming approaches are demonstrated via a number of simulated and experimental examples. Index Terms--Adaptive arrays, array errors, interference suppression, robust adaptive beamforming, robust Capon beamforming, time delay, time reversal, ultrasound imaging.

To detect buried landmines, Planning Systems Incorporated (PSI) has developed a ground-penetrating synthetic aperture radar (GPSAR) system. Since the electromagnetic wave propagation velocity in the soil depends on many factors, velocity uncertainty is inevitable. However, we have observed that, unlike conventional airborne/spaceborne synthetic aperture radar (SAR) systems, the PSI GPSAR system is very robust against the velocity uncertainty under mild conditions. Theoretical analysis is provided to explain this observation. Although our discussion is based on the PSI GPSAR system, it applies to other GPR-based (ground-penetrating radar) landmine detection systems as well.

An acoustic ranging scheme based on the time-of-flight (TOF) principle is described and discussed.

A simple yet effective algorithm called Weighted Fourier transform and RELAXation (WRELAX) is developed to solve a time delay estimation problem. This approach simplifies a complicated nonlinear least squares criterion using relaxation techniques. It utilizes an ultra-wideband ground-penetrating radar to identify and classify roadway subsurface defects. Experimental and numerical examples on the performance of the algorithm are presented.

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 phase 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 false-alarm 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. Index Terms - Adaptive arrays, airborne radar.

A robust autofocus 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 a parametric algorithm based on a very flexible data model which takes into account arbitrary range migration and arbitrary phase errors across the synthetic aperture that may be induced by unwanted radial motion of the target as well as propagation or system instability. AUTOCLEAN can be classified as a multiple scatterer algorithm (MSA), but it differs considerably from other 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 very dominant; 3) phase and RCS (radar cross section) information from each selected scatterer are combined in an optimal way; 4) the troublesome phase unwrapping step is avoided. AUTOCLEAN is computationally efficient and involves only a sequence of FFTs (fast Fourier transforms). Another good feature associated with AUTOCLEAN is that its performance can be progressively improved by assuming a larger number of dominant scatterers for the target. Numerical and experimental results have shown that AUTOCLEAN is a very robust autofocus tool for ISAR imaging.

High range resolution (HRR) moving target indicator (MTI) is becoming increasingly important for many military and civilian applications such as the detection and classification of moving targets in strong clutter background. We consider the problem of extracting the HRR features of moving targets with very closely spaced scatterers in the presence of strong stationary clutter, where the range migration and Doppler 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 flexible 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.

We present an efficient algorithm for high resolution time delay estimation in colored Gaussian noise with multiple looks. By using a relaxation-based optimization approach and exploiting the Toeplitz property of the noise covariance matrix, the enhanced new algorithm can achieve the corresponding Cramer-Rao bounds (CRBs) with a very small number of independent looks.

The complex inverse synthetic aperture radar (ISAR) image formation of maneuvering targets is considered using an adaptive Capon spectral estimation technique. The proposed algorithm utilizes fast Fourier transform and simple matrix operations and is therefore a computationally efficient recursive implementation of the Capon complex spectral estimation model. Both the forward-only and forward-backward Capon methods for time-varying complex spectral estimation are more efficient when the matrix inversion lemma and fast Fourier transform are applied where appropriate than when they are directly implemented.

Time delay estimation of very closely spaced signals is analyzed and a super resolution method called Method Of Direction Estimation-Weighted Fourier transform and RELAXation based (MODE-WRELAX) is described. MODE-WRELAX offers higher resolution than WRELAX and gives better accuracy than MODE. Results indicate that the MODE-WRELAX estimates can approach the corresponding Cramer-Rao bounds as the signal-to-noise ratio increases.