Optimization of point target tracking filters

We review a powerful temporal-based algorithm, a triple temporal filter (TTF) with six input parameters, for detecting and tracking point targets in consecutive frame data acquired with staring infrared (IR) cameras. Using an extensive data set of locally acquired real-world data, we used an iterative optimization technique, the Simplex algorithm, to find an optimum set of input parameters for a given data set. Analysis of correlations among the optimum filter parameters based on a representative subset of our database led to two improved versions of the filter: one dedicated to noise-dominated scenes, the other to cloud clutter-dominated scenes. Additional correlations of filter parameters with measures of clutter severity and target velocity as well as simulations of filter responses to idealized targets reveal which features of the data determine the best choice of filter parameters. The performance characteristics of the filter is detailed by a few example scenes and metric plots of signal to clutter gains and signal to noise gains over the total database