In this paper we develop and analyze high-speed algorithms for the detection of point targets in infrared (IR) images with spatially varying clutter. Current target detection systems are effective at detecting bright targets in a uniform sky, but in areas of strong clutter are either unable to detect targets reliably or are limited by high false alarm rates. We assume that target and sensor models are available. Clutter is considered to be poorly characterized and spatially varying. Target detection algorithms are based on filtering to enhance the target signal relative to the background, followed by an adaptive threshold. Statistical analysis of the algorithms is provided to quantify algorithm performance. Our system implements a spatially adaptive algorithm that maximizes probability of target detection while maintaining a fixed false alarm rate. The algorithms are robust in the presence of spatially varying clutter. We include experimental results to illustrate this.

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