Adaptive detection of range distributed targets

A modified generalized likelihood ratio test (MGLRT) for the adaptive detection of a target or targets that are distributed in range is derived. The unknown parameters associated with the hypothesis test are the complex amplitudes in range of the desired target and the unknown covariance matrix of the additive interference, which is assumed to be characterized as complex zero-mean correlated Gaussian random variables. The target´s or targets´ complex amplitudes are assumed to be distributed across the entire input data block (sensor×range). Results on probabilities of false alarm and detection are derived, and a bounded constant false alarm rate (CFAR) detector is developed. Simulation results are presented. It is shown that the derived MGLRT of range distributed targets is much more effective in detecting targets distributed in range than an M out of K detector, which is cascaded with a single-point target Kelly (1986) detector