The presence of turbulence such as maritime aerosols between the target and the observer degrades the detection and classification performance of electro-optical sensors and detectors. A filtering algorithm that takes into account of the environmental degradation, the background non-overlapping noise, the non-stationarity of the scene, non-target objects and additive system noise is designed and implemented to detect and classify targets under such conditions. The detection performance of this algorithm has been validated using computer simulations and found to be superior to filters that are optimal with respect to noise statistics but do not take into account the effects of environmental conditions. The algorithm is then extended using a set of training images to be distortion-invariant with respect to different target aspects.
A filter function designed to maximize the peak-to-output-energy metric statically for non- overlapping target and overlapping scene noises is extended for use to detect and identify maritime targets within the framework of an automatic target classifier. The filter is designed to be distortion-invariant with respect to different target aspects. Computer simulations show that the filter can successfully detect the distorted true class targets. A set of tilers constructed using subsets of the true class input images were implemented to discriminate target aspects and shown to have reliable performance.
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