Optimal non-coherent detection in K-distributed clutter environment

Abstract

Non-coherent detection of Gaussian targets (Swerling II targets) in the K-distributed clutter environment is investigated. The optimal detector is derived based on the Neyman-Pearson principle. It is shown to be the well-known square-law detector in the domain of multi-pulse process. Temporally correlated clutter provides a target gain, and improves detection. The higher the temporal correlation, the higher the target gain. Spatially correlated underlying clutter texture can also provide a constant false-alarm (CFAR) gain. The autoregressive technique is used to optimally estimate the texture of clutter. That in turn significanly improves the detection compared to the traditional cell-averaging processing in the range domain.