A New Way to Model Nonstationary Sea Clutter

TLDR: By systematically analyzing differentiated data of 280 sea clutter time series measured under various sea and weather conditions, it is shown that the Tsallis distribution fits sea clutter data much better than commonly used distributions for sea clutter such as the K distribution.

Abstract: Sea clutter refers to the radar backscatter from a patch of ocean surface. To properly characterize radar clutter returns, a lot of effort has been made to fit various distributions to the observed amplitude data of sea clutter. However, the fitting of real sea clutter data using those distributions is not satisfactory. This may be due to the fact that sea clutter data is highly nonstationary. This nonstationarity motivates us to perform distributional analysis on the data obtained by differentiating the amplitude data of sea clutter. By systematically analyzing differentiated data of 280 sea clutter time series measured under various sea and weather conditions, we show that the Tsallis distribution fits sea clutter data much better than commonly used distributions for sea clutter such as the K distribution. We also find that the parameters from the Tsallis distribution are more effective than the ones from the K distribution for detecting low observable targets within sea clutter.