Non-Gaussian clutter modeling with generalized spherically invariant random vectors

This paper describes the modeling of non-Gaussian clutter with a set of generalized spherically invariant random vectors (SLRV's). The generalization extends the traditional model to account for dependence between successive SIRV realizations, Significant properties of generalized SIRV's are derived, as well as a closed-form expression for a family of generalized SIRV density functions, The density underlying recorded sonar reverberation is approximated with this function through appropriate choice of a shape parameter, Given this reverberation model, the optimum detector is derived from the generalized SIRV density likelihood ratio, This paper concludes bf showing how applying this optimum detector to non-Gaussian data leads to a reduction in the false alarm rate when compared to processing with a matched filter alone.