Model-based adaptive detection of range-spread targets

Abstract

The authors consider the problem of detecting range-distributed targets in the presence of structured disturbance modelled as an autoregressive Gaussian process with unknown parameters. The focus is on two different scenarios. The first assumes that all the data vectors from the cells under test share the same covariance matrix (homogeneous environment). The second refers to the case of data vectors characterised by completely different covariances (heterogeneous environment). Four detectors exploiting the asymptotic generalised likelihood ratio criterion are devised and assessed. Remarkably, they ensure the constant false alarm rate (CFAR) property with respect to the disturbance power level, and two of them are asymptotically CFAR with respect to the disturbance covariance matrix. Finally the performance assessment, based also on real radar data, has shown that these detectors achieve, in general, satisfactory detection performances.