Characterization of Sea Clutter Based on Estimated Space-time Covariance Matrix from Real Dataset

We propose an estimation method for the space-time covariance matrix of sea clutter to support the application of waveform-agile sensing procedures that rely on accurate estimation of this matrix. The method exploits the special structure of the vectorized states of the scattering function for the dynamical system model governing the temporal evolution of the clutter matrix followed by a multiple particle filtering approach to estimate the covariance matrix and deal with the high dimensionality on the formulation. The effectiveness of the method is demonstrated by estimating the scattering function covariance matrix of both simulated sea clutter data and real sea clutter data from DSTO INGARA radar; and detecting a small moving target embedded in the clutter.