Post-processing of point target detection sinusoidal filters

In previous conferences, we described a powerful class of temporal filters with excellent signal to clutter gains in evolving cloud scenes of consecutive IR sequences. The generic temporal filter is a zero-mean damped sinusoid, implemented recursively. The full algorithm, a triple temporal filter (TTF), consists of a sequence of two zero-mean damped sinusoids followed by an exponential averaging filter. The outputs of the first two filters are weakened at strong local edges. Analysis of a real-world database led to two optimized filters: one dedicated to noise-dominated scenes, the other to cloud clutter-dominated scenes; a dual-channel fusion of the two filters has also been implemented in hardware. This paper describes the post-processing and thresholding of the outputs of the filter algorithms. Post-processing on each output frame is implemented by a simple spatial algorithm which searches for maximum linear or pseudo-linear streaks made up of three linked pixels. The output histogram after post-processing is more robust to histogram-based thresholding and in some cases has improved signal to clutter ratio. The threshold is based on a simple level-occupancy (binary) histogram in which the first gap of 4 empty levels is determined and a threshold established based on this gap value and the number of occupied levels in the histogram above the gap. The post-processing and thresholding of the filter outputs are now operating in real-time hardware. Preliminary flight tests on a small aircraft of the algorithms in real-time operation demonstrate the viability of the approach on a moving platform. Specific examples and a video of the real-time performance on a fixed and moving platform will be presented at the conference.