Abstract

Modeling sea clutter is a difficult problem due to the interaction of the sea surface characteristics (wind speed and wind direction), the geometry of acquisition (grazing angle and azimuth angle), and radar parameters (frequency, polarization, and resolution). In synthetic aperture radar (SAR) imagery, the effect of coherent averaging and motion from the sea will also influence the statistics. An accurate description of the sea surface amplitude probability density function is important for robust target detection. The goal of this paper is to identify a common distribution which matches two different airborne SAR X-band data sets obtained from different locales and at different grazing angles. The first data set was collected by the French Aerospace Laboratory (ONERA) SETHI radar off the coast of France, at low grazing angles (3° and 10°). The second is the Ingara medium grazing angle (15°-45°) sea clutter data set collected by the Defence Science and Technology Organisation off the coast of Australia. In this paper, a number of recently developed distributions are considered, including the K+Rayleigh and Pareto+noise, both of which account for thermal noise. To measure the effectiveness of the model fit, the Bhattacharyya distance and the threshold error are computed with particular attention given to the tail region, where the threshold is determined in a detection scenario.

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