Non-stationary sea clutter: Impact on disturbance covariance matrix estimate and detector CFAR

Greco, Maria; Gini, Fulvio; Younsi, Arezki; Rangaswamy, Muralidhar; Zoubir, Abdelhak M.

doi:10.1109/radar.2008.4653986

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Data from further bistatic angles are required to develop a full understanding of the variation in the statistics of the backscattered signals. It would also be of interest to apply the methods seen in [4] to the comparative bistatic/monostatic spectrograms to gain a deeper understanding of the nonstationary effects within bistatic sea clutter.…”

Section: Discussionmentioning

confidence: 99%

Coherent analysis of horizontally-polarized monostatic and bistatic sea clutter

Ritchie

Al-Ashwal

Stove

et al. 2012

IET International Conference on Radar Systems (Radar 2012)

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Section: Discussionmentioning

confidence: 99%

Coherent analysis of horizontally-polarized monostatic and bistatic sea clutter

Ritchie

Al-Ashwal

Stove

et al. 2012

IET International Conference on Radar Systems (Radar 2012)

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“…The advantage of working with low values of N is to ensure the temporal stationarity of the clutter. In fact, previous analysis of recorded real sea clutter data [27,28], showed that this process is non-stationary, both in time and space. In addition, the aforementioned figures show that the knowledge of the fraction of the cells under test containing the target, is very important (see Table 1).…”

Section: Performance In Terms Of Detection Probabilitymentioning

confidence: 99%

Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data

Mennad

Younsi

Korso³

et al. 2017

Digital Signal Processing

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“…To cope with this uncertainty, the random clutter texture component is treated as a deterministic and unknown parameter. The first candidate for the independent model is the normalized sample covariance matrix (NSCM) estimator [26,33]. Here, we normalize each term {x k (n)x H k (n)} in the sums in (24) by the data-dependent normalization factorτ k NSCM in place of true τ k to obtain…”

Section: Adaptive Cg-pglrt Detector For Unknown Ar Parametersmentioning

confidence: 99%

Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes

Yan

et al. 2012

EURASIP J. Adv. Signal Process.

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In this article, we consider the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally colored compound-Gaussian disturbance. By modeling the disturbance as a multichannel autoregressive (AR) process, we first derive a parametric generalized likelihood ratio test against compoundGaussian disturbance (CG-PGLRT) assuming that the true multichannel AR parameters are perfectly known. For the two-step GLRT design criterion, we combine the multichannel AR parameter estimation algorithm with three covariance matrix estimation strategies for compound-Gaussian environment, then obtain three adaptive CG-PGLRT detectors by replacing the ideal multichannel AR parameters with their estimates. Owing to treating the random texture components of disturbance as deterministic unknown parameters, all of the proposed detectors require no a priori knowledge about the disturbance statistics. The performance assessments are conducted by means of Monte Carlo trials. We focus on the issues of constant false alarm rate (CFAR) behavior, detection and false alarm probabilities. Numerical results show that the proposed adaptive CG-PGLRT detectors have dramatically ease the training and computational burden compared to the generalized likelihood ratio test-linear quadratic (GLRT-LQ) which is referred to as covariance matrix based detector and relies more heavily on training.

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Non-stationary sea clutter: Impact on disturbance covariance matrix estimate and detector CFAR

Cited by 13 publications

References 15 publications

Coherent analysis of horizontally-polarized monostatic and bistatic sea clutter

Coherent analysis of horizontally-polarized monostatic and bistatic sea clutter

Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data

Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes

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