Superresolution HRR ATR with high definition vector imaging

Nguyen, Duy Hai; Benitz, Gerald R.; Kay, John H.; Orchard, Bradley J.; Whiting, Robert H.

doi:10.1109/7.976964

Cited by 15 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 These data sets have previously been used in a number of studies involving HRR profile formation and HRR ATR. [2][3][4][5][6] We compare the results of our method to those obtained by Capon's method 23 and RELAX, 24 as well as conventional Fourier-transformbased reconstructions. Capon's method and RELAX have played central roles in a number of recent techniques including those in Ref.…”

Section: Hrr Datamentioning

confidence: 99%

“…Furthermore, the study in Refs. 5,6 has also demonstrated that superresolution techniques can improve HRR ATR performance. With these motivations, we propose a non-quadratic regularization-based superresolution signal reconstruction technique for the formation of HRR profiles.…”

Section: Motivation For Superresolution Processingmentioning

confidence: 99%

“…[1][2][3][4][5][6] The success of ATR depends on the resolution and accuracy of the target range features in the HRR profiles. Conventionally, HRR profiles are formed from the phase history data through a Fourier transform-based operation, which has a resolution limited by the radar bandwidth.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

<title>Formation of HRR profiles by nonquadratic optimization for improved feature extraction</title>

Çetin¹,

Karl²,

Castañón³

2002

Algorithms for Synthetic Aperture Radar Imagery IX

View full text Add to dashboard Cite

We propose a new method for superresolution, feature-enhanced reconstruction of high range-resolution (HRR) radar profiles. We pose the problem of the formation of the HRR profiles from phase history data as an optimization problem. Resolution and feature enhancements are achieved by imposing non-quadratic regularizing constraints on the solution of the optimization problem. We present experimental results on synthetic and measured data, and compare the proposed method to currently available techniques. This analysis shows the ability of the proposed method to preserve high-resolution features such as the locations and amplitudes of the dominant scatterers in the HRR profile. This suggests that the technique may potentially help improve the performance of HRR target recognition systems.

show abstract

Section: Hrr Datamentioning

confidence: 99%

Section: Motivation For Superresolution Processingmentioning

confidence: 99%

See 1 more Smart Citation

<title>Formation of HRR profiles by nonquadratic optimization for improved feature extraction</title>

Çetin¹,

Karl²,

Castañón³

2002

Algorithms for Synthetic Aperture Radar Imagery IX

View full text Add to dashboard Cite

show abstract

“…The model (6) applies to a stationary target and a single incident wave that starts at position x at time t. We now assume that the incident field is a series of pulses, beginning at times t = θ n , n = 1, 2, . .…”

Section: The Kirchhoff Approximationmentioning

confidence: 99%

<title>Microlocal high-range-resolution ISAR for low signal-to-noise environments</title>

Cheney

Borden

2003

SPIE Proceedings

View full text Add to dashboard Cite

We consider the problem of identification of airborne objects from high-range-resolution radar data. We use high-frequency asymptotics to show that certain features of the object correspond to identifiable features of the radar data. We study the cases of single scattering and scattering from re-entrant structures such as ducts.This work suggests a method for target identification that circumvents the need to create an intermediate radar image from which the object's characteristics are to be extracted. As such, this scheme may be applicable to efficient machine-based radar identification programs.

show abstract

“…When this model is inappropriate-as in the case of structural dispersion-the usual images will contain artifacts. (One approach to handling non-point-like scatterers is High Definition Vector Imaging (HDVI) [1], [15].) What is more generally needed is a nontraditional fitting method that can be applied in data space not only to aid in the imaging of general scatterers but also to facilitate the range alignment process.…”

mentioning

confidence: 99%

Microlocal ISAR for Low Signal-to-Noise Environments

Borden

Cheney

2006 IEEE Conference on Radar

View full text Add to dashboard Cite

Abstract-The problem of extracting radar target information from multi-aspect high-range-resolution data is examined. We suggest a new non-imaging approach that is based on microlocal analysis, which is a mathematical theory developed to handle highfrequency asymptotics. In essence, we relate features of the target to high-frequency components of the data. To deal with realistic band-limited data, we propose an iterative algorithm (based on the generalized Radon-Hough transform) in which we estimate the high-frequency features of the data, one after another, and subtract out the corresponding band-limited components. The algorithm has been successfully tested on noisy data, and may have a number of advantages over conventional imaging methods.

show abstract

Superresolution HRR ATR with high definition vector imaging

Cited by 15 publications

References 16 publications

<title>Formation of HRR profiles by nonquadratic optimization for improved feature extraction</title>

<title>Formation of HRR profiles by nonquadratic optimization for improved feature extraction</title>

<title>Microlocal high-range-resolution ISAR for low signal-to-noise environments</title>

Microlocal ISAR for Low Signal-to-Noise Environments

Contact Info

Product

Resources

About