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Digital Filters For The Detection Of Resolved And Unresolved Targets Embedded In Infrared (IR) Scenes

By applying the concept of analogue matched filtering to spatially correlated background scenes and making the assumption that they are characterized by a first order Markov process, a simple but powerful digital filter is derived. Direct inspection of this filter results in some useful insights into the process of spatial filtering. The usefulness of this filter is further demonstrated by showing its clutter reduction capability on a set of five measured infrared scenes representing a wide variety of terrain and weather conditions. Also, matched digital filters are obtained directly for each of the five measured scenes making no assumption about the spectral content of the backgrounds. Although the resulting filters sometimes differ in form from those obtained using the first order Markov process assumption, their performance is shown to be nearly identical, proving that this assumption provides a useful model for IR backgrounds. Finally, a technique is developed to construct matched filters to detect resolved targets whose size and intensity distributions are known only statistically. Examples of such targets are constructed and the clutter reduction properties of these filters are quantitatively evaluated.