Self-cohering airborne distributed arrays on land clutter using the robust minimum variance algorithm

“…In the first case the range bin data from the K scatterers in different range bins can be combined to form a single, virtual dominant scatterer from which a high-quality weight vector can often be obtained. This modification to the DSA is due to Attia and called by him the robust minimum variance algorithm (RMVA) [14]. In Ref.…”

Section: Dominant Scaiterer Algorithmmentioning

confidence: 99%

“…Also, because the integral of a power density spectrum is the total power in the process, the objective function J, is the zero-lag value of the Fourier transform of p ( u ) g ( u ) . or (14) where, as bcfore, F means Fourier transform. Now…”

Section: (7)mentioning

confidence: 99%

Self‐calibration of large phased‐array antennas for radar

Steinberg

1992

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Self-calibration of a phased antenna array is required when distortions in the array are not known a priori and cannot be measured, or when turbulence in the propagation medium perturbs the radiation field. Two types of self-calibration procedures are discussed that have proved successful in experimental high-resolution two-dimensional microwave radar imaging. Each extracts information from the backscattered reradiation field to deduce a compensating weight vector for the phased array antenna. The first depends upon the presence of a strong reflector in the field of view of the transmitter.The second calibrates the array from correlation estimates of wavefront samples in the array. The basic algorithm in each group is described, along with two more sophisticated algorithms in the latter class. Two-dimensional radar images of airplanes are shown with resolution comparable to human vision. The performance of each algorithm and comparisons between them are illustrated by these images of targets and by simulation experiments. 0 1993 John Wiley 8 Sons, Inc.

show abstract

“…Range cell realignment is considered to be routine and is based upon, for instance, the correlation method (see Chen and Andrews [1]) or the minimum-entropy method (see Wang and Bao [2]). Phase autofocus is more stringent in its requirements and many nonparametric methods have been proposed, most of which track the phase history of an isolated dominant scatterer (prominent point processing (PPP), see Steinberg [3]) or the centroid of multiple well-isolated scatterers (multiple scatterer algorithm (MSA), see Carrara et al [4], Haywood and Evans [5], Wu et al [6], Attia [7]). The phase-gradient algorithm (PGA, see Wahl et al [8]) is another popular nonparametric technique, which iteratively estimates the residual phase by integrating over range an estimate of its derivative (gradient).…”

Section: Introductionmentioning

confidence: 99%

Eigenspace-Based Motion Compensation for ISAR Target Imaging

Yau

Berry

Haywood

2006

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

A novel motion compensation technique is presented for the purpose of forming focused ISAR images which exhibits the robustness of parametric methods but overcomes their convergence difficulties. Like the most commonly used parametric autofocus techniques in ISAR imaging (the image contrast maximization and entropy minimization methods) this is achieved by estimating a target's radial motion in order to correct for target scatterer range cell migration and phase error. Parametric methods generally suffer a major drawback, namely that their optimization algorithms often fail to converge to the optimal solution. This difficulty is overcome in the proposed method by employing a sequential approach to the optimization, estimating the radial motion of the target by means of a range profile cross-correlation, followed by a subspace-based technique involving singular value decomposition (SVD). This two-stage approach greatly simplifies the optimization process by allowing numerical searches to be implemented in solution spaces of reduced dimension.

show abstract

Self-cohering airborne distributed arrays on land clutter using the robust minimum variance algorithm

Cited by 13 publications

References 1 publication

Inverse synthetic aperture radar imaging of satellites

Inverse synthetic aperture radar imaging of satellites

Self‐calibration of large phased‐array antennas for radar

Eigenspace-Based Motion Compensation for ISAR Target Imaging

Contact Info

Product

Resources

About