Robust target identification in white Gaussian noise for ultra wide-band radar systems

Mooney, J.E.; Ding, Zhi; Riggs, L.S.

doi:10.1109/8.743818

Cited by 35 publications

(45 citation statements)

References 11 publications

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“…The identification of radar targets can be achieved using various radar signatures, such as natural frequencies, high resolution range (HRR) profiles and inverse synthetic aperture radar (ISAR) images. Natural frequencies due to the resonance phenomena of a target are insensitive to its relative orientation, resulting in radar target identification with robust features [10][11][12][13]. However, in a real situation with noise and clutter signals, as well as the target signals, the accurate estimation of these natural frequencies is hard to achieve because their information is included in the late time responses, which are easily corrupted by noise and clutter [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Isar Imaging Algorithms for Radar Target Identification

Park¹,

Kim²

2010

PIER

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Abstract-Inverse synthetic aperture radar (ISAR) images represent the two-dimensional (2-D) spatial distribution of the radar crosssection (RCS) of an object and, thus, they can be applied to the problem of target identification. The traditional approach to ISAR imaging is the range-Doppler algorithm based on the 2-D Fourier transform. However, the 2-D Fourier transform often results in poor resolution ISAR images, especially when the measured frequency bandwidth and angular region are limited. Instead of the Fourier transform, high resolution spectral estimation techniques can be adopted to improve the resolution of ISAR images. These are the autoregressive (AR) model, multiple signal classification (MUSIC), and matrix enhancement and matrix pencil MUSIC (MEMP-MUSIC). In this study, the ISAR images from these high-resolution spectral estimators, as well as the FFT approach, are identified using a recently developed identification algorithm based on the polar mapping of ISAR images. In addition, each ISAR imaging algorithm is analyzed and compared in the framework of radar target identification. The results show that the dynamic range as well as the resolution of the ISAR images plays an important role in the identification performance. Moreover, the optimum size of the subarray (i.e., covariance matrix) for MUSIC and MEMP-MUSIC in terms of target identification is experimentally derived.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study on Isar Imaging Algorithms for Radar Target Identification

Park¹,

Kim²

2010

PIER

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“…Mittra, Pearson, and Van Blaricum first proposed using the Splane for target discrimination [21]. Some of the resonance based ATR schemes that are popular in the literature include K-pulse [22], [23], E-pulse [16], [17], and Generalized Likelihood Ratio Test (GLRT) [24], [25]. Amongst them, the GLRT has produced a better identification result, in the presence of noise [24], [25].…”

Section: Systemmentioning

confidence: 99%

“…Recently, the use of a GLRT, which is based on maximum likelihood estimation of the model parameters in white Gaussian noise, was shown to perform better than the E-pulse technique [24], [25]. Moreover, the GLRT method is more computationally efficient than E-pulse technique.…”

Section: Chapter 2: Resonance Based Rader Target Identificationmentioning

confidence: 99%

“…In addition, the target producing the return belongs to a family of known targets for which we know a priori the poles of each for classification purposes. The mathematical definition of GLRT can be found in the literature [24], [25].…”

Section: Generalized Likelihood Ratio Testmentioning

confidence: 99%

“…aircrafts etc. [8], [16], [24], [32], [39], [92]. This is necessary in the first instance to relate resonances to various dimensions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radar target identification based on complex natural resonances

Chen¹

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Efficient feature extraction from time-frequency analysis of transient response for target identification

Choi

2000

Microw. Opt. Technol. Lett.

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In this letter, we present a new feature extraction method, for radar target identification, that use both an early‐time and a late‐time response simultaneously. A time–frequency analysis is used for feature extraction from the transient target response, and the dimension of feature vectors is reduced by a novel method that we propose. Simulation for target identification is performed with three different swept‐wing aircraft models. Results show that the proposed feature extraction method has good identification performance in the presence of white Gaussian noise. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 403–407, 2000.

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Robust target identification in white Gaussian noise for ultra wide-band radar systems

Cited by 35 publications

References 11 publications

A Comparative Study on Isar Imaging Algorithms for Radar Target Identification

A Comparative Study on Isar Imaging Algorithms for Radar Target Identification

Radar target identification based on complex natural resonances

Efficient feature extraction from time-frequency analysis of transient response for target identification

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