Tracking point targets in cloud clutter

To realize the potential of modern staring IR technology as the basis for an improved IRST one requires better algorithms for detecting unresolved targets moving at fractions of a pixel per frame time. While available algorithms for such targets in white noise are reasonably good, they have high false alarm rates in non-stationary clutter, such as evolving clouds. We review here a new class of temporal filters which have outstanding signal to clutter gains in evolving clouds and still retain goad signal to temporal noise sensitivity in blue sky or night data. The generic temporal filter is a damped sinusoid, implemented recursively, Our final algorithm, a triple temporal filter (TTF) based on six parameters, consists of a sequence of two damped sinusoids followed by an exponential averaging filter, along with an edge suppression feature. Initial tests of the TTF filter concept demonstrated excellent performance in evolving cloud scenes. Three ''trackers'' based an the TTF operate in real-time hardware on laboratory TR cameras including an empirical initial version; and two recent forms identified by an optimization routine. The latter two operate. best in the two distinct realms: one for evolving cloud clutter, the other for temporal noise-dominated scenes such as blue sky or stagnant clouds. Results are presented both as specific examples and metric plots over an extensive database of local scenes with targets of opportunity.