Signal-processing algorithms for point-target detection in consecutive-frame staring imagery

Abstract

Our algorithm development for point target surveillance is closely meshed to our laboratory IR cameras. The two-stage approach falls into the category of `track before detect' and incorporates dynamic programming optimization techniques. The first stage generates merit scores for each pixel and suppresses clutter by spatial/temporal subtractions from N registered frames of data. The higher the value of the merit score, the more likely that a target is present. In addition to the merit score, the best track associated with each score is stored; together they comprise the merit function. In the second stage, merit functions are associated and dynamic programming techniques are used to create combined merit functions. Nineteen and thirteen frames of data are used to accumulate merit functions. Results using a total of 38 and 39 frames of data are presented for a set of simulated targets embedded in white noise. The result is a high probability of detection and low false alarm rate down to a signal to noise ratio of about 2.0. Preliminary results for some real targets (extracted from real scenes and then re- embedded in white noise) show a graceful degradation from the results obtained on simulated targets.