The track-before-detect processing technique has been employed in numerous computer vision based algorithms addressing the dim target detection problem. This processing technique has been shown to be effective under certain conditions; but in particularly noisy or highly cluttered environments, detection performance may be improved by introducing an image preprocessing stage to enhance the raw sensor measurements prior to integration. In this paper, we compare the 'Close-Minus-Open' (CMO) and 'Preserved-Sign' (PS) morphological image preprocessing techniques for suppressing unwanted noise and emphasising target features in the measurement images. This investigation is motivated by the unmanned aerial vehicle "sense-and-avoid" application, where morphology-based filters have demonstrated a degree of success in the detection of small point-like features that may correspond to collision-course aircraft. For completeness, we also briefly examine two well published track-before-detect temporal filtering techniques which may be combined with the morphological pre-processing to detect dim, sub-pixel sized targets. Results from our simulation studies show that the PS approach achieves a higher detection rate than the CMO approach.
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