Adaptive Radar Detection of Distributed Targets in Homogeneous Noise plus Subspace Interference

This paper addresses adaptive radar detection of distributed targets embedded in homogeneous Gaussian noise and interference, which is assumed to belong to an either known or unknown subspace of the observables. At the design stage we resort to either the GLRT or the so-called two-step GLRT-based design procedure and assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as zero-mean, complex normal ones, sharing the same covariance matrix. The common covariance matrix is unknown at the receiver. The performance assessment, carried out by Monte Carlo simulation, confirms the effectiveness of the newly-proposed detection algorithms also in comparison to previously-proposed ones