CFAR detection of spatially distributed targets in k-distributed clutter with unknown parameters

The paper deals with Constant False Alarm Rate (CFAR) detection of spatially distributed targets embedded in K-distributed clutter with correlated texture and unknown parameters. The proposed Cell Averaging-based detector automatically selects the suitable pre-computed threshold factor in order to maintain a prescribed Probability of False Alarm pfa. The threshold factors should be computed offline through Monte Carlo simulations for different clutter parameters and correlation degrees. The online estimation procedure of clutter parameters has been implemented using Maximum Likelihood Moments approach. Performances analysis of the proposed detector assumes unknown shape and scale parameters and Multiple Dominant Scattering centers model (MDS) for spatially distributed targets.