Sparse network array processing employing prior covariance knowledge

The paper examines an alternative multidimensional adaptive array processing architecture which provides a unique highly-parallelizable algorithm suitable for distributed processing. The principle is to perform interference cancellation on each element using a different sparse sampling of the remaining elements auxiliary inputs. By doing so, special correlations in the data can be exploited to significantly reduce the degrees of freedom required in each adaptive process. This reduces both the computation count and the number of samples required for adaptivity. An example space-time adaptive nulling application of airborne clutter shows near optimal performance with a factor of four computational savings over equivalent space-time techniques