False detection of chaotic behaviour in the stochastic compound k-distribution model of radar sea clutter

There is current debate in the radar community whether sea clutter is stochastic or chaotic. In this paper, a stochastic k-distributed surrogate is generated for a typical sea clutter data set. The k-distributed set was then analysed using the methods recently applied to sea clutter by Haykin et al. (Haykin and Li, Proc. IEEE, vol.83, pp.95-122, 1995; Haykin and Puthusserypady, Proc. IEE Radar, pp.75-9, 1997). The k-distributed set is shown to have DML (maximum likelihood estimation of the correlation dimension) and FNN (false nearest neighbours) values in the same range as reported by Haykin et al. (1995; 1997) and with positive and negative Lyapunov exponents. In addition, various white and correlated noise distributed sets are analysed in the same way and found to produce a similar artefact. It is concluded that these chaotic invariants cannot be used to distinguish between chaotic and stochastic time series and are redundant in an application, such as radar sea clutter, where the time series is unknown and could be of a stochastic nature