Cyclostationary signal models for the detection and characterization of vibrating objects in SAR data

EURASIP J. Adv. Signal Process.

et al. 2012

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.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimation for SAR micromotion target based on sparse signal representation

Zhu

Mohammad-Djafari

EURASIP J. Adv. Signal Process.

et al. 2012

“…As it is impractical, for the ghost points are in effect indistinguishable from real stationary targets, the algorithm implemented the detection of SAR micro-motion targets for the first time to our knowledge. In [5], a generalised likelihood ratio test (GLRT) detector was derived to detect vibrating targets in SAR imagery and the cyclic spectral density was used to estimate the vibration amplitude and frequency. GLRT is effective, but it has huge computational load and small tolerance to a model mismatch.…”

Section: Introductionmentioning

confidence: 99%

“…It requires high SCNR to obtain fine spectra. The cyclic spectral density method [5] dispenses with limitations to the signal length, but it is not suited to multiple targets possessing different micro-motion periods, neither are the methods in [11,12] mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Pulse-repetition-interval transform-based vibrating target detection and estimation in synthetic aperture radar

Deng

Qin

et al. 2012

IET Signal Process.

A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority.

“…Unpractical as it is, for the ghost points are in effect indistinguishable from true stationary targets, the algorithm implemented the detection of SAR micromotion targets for the first time to our knowledge. In [4] and [5], GLRT detectors were derived to detect micromotion targets in SAR imagery or SAR returns. GLRT is applied to every range cell which may contain micromotion-induced sinusoidal frequency-modulated signals, and therefore this approach desponds on SAR data without range cell migration (RCM) or RCM-compensated data.…”

Section: Introductionmentioning

confidence: 99%

SAR micromotion target detection based on gapped sine curves

Deng

2012 IEEE International Geoscience and Remote Sensing Symposium

et al. 2012

This paper presents a detection algorithm for micromotion targets, such as rotating antennas, in the synthetic aperture radar (SAR) geometry. It utilizes target's range cell migration characteristics, i.e. a micromotion target takes on a sine cure in the slowtime-range image, but with incontinuity or gaps when clutter (and part of target energy) is suppressed. Two-stage detection, including MTI and the extended Hough transform, is used to suppress clutter and to extract the sine curve. Quasi-real SAR data of Isleta Lake demonstrate high SNR gains and good detection performance.