ROBUST MIPA ARRAY PROCESSORS WITH BIVARIATE AND MARKOV DEPENDENCE

The effects of dependent sampling on the sequential m-interval polynom ial approximation (MIPA) array processing detectors are considered. Th is problem is approached on a weak signal basis; bivariate and Markovi an types of dependence are used separately to model the dependence amo ng the data samples. For practical implementations; we assume that the noise is independent between sensors; we will allow only temporal dep endence to exist among the data collected by each sensor. Based on the relative efficiency criteria, the performance of these detectors is c ompared to that of their corresponding independent noise counterpart. It is concluded that improved performance is achieved.