The detection of small targets in infrared (IR) clutter is a problem of critical importance to Infrared Search and Track (IRST) systems. This paper presents techniques for analyzing and improving the detection performance of IRST systems. Only spatial, or single-frame, processing will be addressed. For clutter with spatially slowly varying statistics, the approach is based on linear filtering. Models of target and cluttter are developed and used to analyze matched filter performance and sensitivity. This sensitivity analysis is used to improve filter bank design. A clutter classification scheme which can separate clutter of different types is presented. Finally, to improve system performance in the presence of large intensity gradients, such as cloud edges, an improved adaptive threshold scheme is presented.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.