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This letter addresses high-resolution sea clutter modelling. Radar clutter modelling is essential to develop constant false alarm rate (CFAR) detection schemes. In this paper, we propose a compound model by the use of the inverted exponentiated Rayleigh (IER) distribution as texture component. The probability density function (PDF) of the compound inverted exponentiated Rayleigh (CIER) distribution is a function of three parameters including the thermal noise power. To assess the modelling performance of the CIER distribution, several tests have been performed using the Intelligent PIXel Processing Radar (IPIX) database where the fitted PDFs and the complimentary cumulative density functions (CCDFs) of the CIER, the well-known compound K and the compound inverse Gaussian (CIG) distributions are compared with the real PDF and CCDF. The results obtained validate the ability of the CIER distribution to model high-resolution sea clutter. [ABSTRACT FROM AUTHOR]

This paper deals with radar constant false alarm rate (CFAR) detection for multiple pulses for gamma-distributed clutter. In this work, some CFAR detectors are extended to cover the case of multiple pulses. Moreover, using the proprieties of the gamma distribution, the expressions of the false alarm probability are also generalized to multiple pulses transmission. The obtained results show the detection performance of multiple pulses generalized detectors – cell averaging-CFAR, greatest of-CFAR and order statistic-CFAR – compared with their counterpart in single-pulse detectors. [ABSTRACT FROM AUTHOR]

Estimators of clutter models parameters based upon higher order moments estimator (HOME) produce usually poor results in particular for low sample sizes. In an attempt to remedy this situation, closed forms of [zlog(z)] and fractional order moments estimator (FOME) are derived in this work and yield a good estimation accuracy of parameters of the compound Gaussian clutter with log-normal texture (CG-LNT). Using simulated and real data, estimation comparisons show that best values of mean square error (MSE) and bias are achieved using the proposed procedures. [ABSTRACT FROM AUTHOR]