Abstract
A class of temporal filters is presented for use with a staring infrared camera in detecting and tracking weak point targets moving slowly in evolving cloud clutter. The generic temporal filter, originally suggested by the singular value decomposition of consecutive frame data, is a zero mean damped sinusoid which can be recursively implemented in the complex plane. From this filter type, a composite triple temporal filter (TTF) is developed, consisting of two sinusoids of different periods in sequence followed by a third (averaging) filter. The TTF achieves impressive cloud clutter suppression by responding strongly to pixel temporal responses caused by moving point targets and weakly to responses caused by cloud edges moving into or out of pixels. An extensive database of local airfield scenes with targets of opportunity taken with two laboratory staring IR cameras was used in the design and testing of the filters. Issues and trade-offs in choosing the parameters of the TTF are explored by comparing two specific forms of the filter: the first based on a damped sinusoid with a period of 16 frames followed by one with a 10 frame period; the second filter has corresponding periods of 40 followed by 30 frames. The first TTF is very effective with targets having velocities from 0.1–0.5 pixels/frame in daytime drifting cloud scenes. However, target signal-to-noise values of ⩾6 are required for detection in white noise (close to blue-sky conditions). The second TTF is more sensitive to slower, weaker targets in blue-sky or cloudless night scenes; however, in order to operate in daytime cloud scenes, spatial enhancements are required. Results are detailed for some representative scenes and given as well for the total database as signal-to-clutter gain plots based on a newly formulated antimedian metric.
Published Version
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