Optimal Detection in the K-Distributed Clutter Environment -- Non-Coherent Radar Processing

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. Amplitude detector, log detector, and the like are not optimal, and result in some detection loss. Temporally correlated clutter provides a target gain, and improves detection. The higher the temporal correlation, the higher the target gain. Spatially correlated non-Gaussian clutter can also provide a CFAR gain. The autoregressive technique is used to optimally estimate the texture of the clutter. That in turn significanly improves the detection compared to the traditional cell-averaging processing. RELEASE LIMITATION Approved for public release