Micro-Doppler analysis of a rotating target in synthetic aperture radar

Thayaparan, T.; Suresh, P.; Qian, Shie; Venkataramaniah, K.; SivaSankaraSai, S.; Sridharan, Karthik

doi:10.1049/iet-spr.2009.0094

Cited by 43 publications

(28 citation statements)

References 12 publications

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“…AGR can give a joint T -F distribution which is non-negative, adaptive and cross-term interference free. The advantage of AGR processing for radar applications has been well described in [14,15]. Up to now, AGR has been successfully applied in ISAR imaging, complicated scattering diagnostic, and radar target classification.…”

Section: Hrr Profile Time-frequency Analysis Based On Agrmentioning

confidence: 99%

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HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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Abstract-A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

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Section: Hrr Profile Time-frequency Analysis Based On Agrmentioning

confidence: 99%

“…T -F analysis techniques have long been used to reveal the complicated scattering mechanisms concealed in radar echoes [15]. In [16], T -F geometrical moments features extraction method is derived for radar target classification.…”

Section: Introductionmentioning

confidence: 99%

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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“…A few algorithms have been proposed for the estimation of SAR micromotion targets [1,[4][5][6]. All of them manipulate a single range cell and take micromotion target azimuthal echoes as sinusoidal frequency modulated (SFM) signals.…”

Section: Introductionmentioning

confidence: 99%

“…Micromotion is mainly embodied by rotation and vibration, and typical SAR micromotion targets include ground/ship-borne search antennas for air traffic control/surveillance [1], rotor blades of hovering helicopters, and vibrating vehicles as well as their tires/ engines. Micromotion parameters, such as the rotating frequency and radius, record targets' attributed information.…”

Section: Introductionmentioning

confidence: 99%

“…The cyclic spectral density [4], a time-frequency method [6], and the adaptive optimal kernel one [5], have been used to estimate the vibrating frequency of simulated or real SAR targets. Then in [1], the wavelet or chirplet decomposition is used to separate the signal of a rotating radar dish from that of stationary clutter and then auto correlation is utilized to get its rotating frequency. All these methods, however, haven't addressed the aforementioned two key problems ever-present in SAR, i.e., clutter and RCM, which hinders their application in reality.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation for SAR micromotion target based on sparse signal representation

Zhu

Mohammad-Djafari

Wang

et al. 2012

EURASIP J. Adv. Signal Process.

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In this article, we address the parameter estimation of micromotion targets in synthetic aperture radar (SAR), where scattering parameters and micromotion parameters of targets are coupled resulting in a nonlinear parameter estimation problem. The conventional methods address this nonlinear problem by matched filter, which are computationally expensive and of lower resolutions. In contrast, we address this problem by linearizing the forward model as a linear combination of elements of an over-complete dictionary. The essential idea of sparse signal representation models comes from the fact that SAR micromotion targets are sparsely distributed in the observation scene. Accordingly, we propose to jointly estimate the target micromotion and scattering parameters via a Bayesian approach with sparsity-inducing priors. In addition, we present a variational approximation framework for Bayesian computation. Numerical simulations demonstrate the proposed sparsity-inducing reconstruction method achieves higher resolution and better performance with smaller measures compared to conventional methods.

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Precession-Identification Based on Sparse Representation

You

Wang

2013

Lecture Notes in Electrical Engineering

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Micro-Doppler analysis of a rotating target in synthetic aperture radar

Cited by 43 publications

References 12 publications

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Parameter estimation for SAR micromotion target based on sparse signal representation

Precession-Identification Based on Sparse Representation

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