Digital camouflage

Multi-scale camouflage is a type of military camouflage combining patterns at two or more scales, often (though not necessarily) with a digital camouflage pattern created with computer assistance. The function is to provide camouflage over a range of distances, or equivalently over a range of scales (scale-invariant camouflage), in the manner of fractals, so some approaches are called fractal camouflage. Not all multiscale patterns are composed of rectangular pixels, even if they were designed using a computer. Further, not all pixellated patterns work at different scales, so being pixellated or digital does not of itself guarantee improved performance.

The root of the modern multi-scale camouflage patterns can be traced back to 1930s experiments in Europe for the German and Soviet armies. Digital patterns date to the 1970s with work by U.S. Army officer Lt. Col. Timothy O'Neill for camouflaging armoured vehicles. This was followed by Canadian development of Canadian Disruptive Pattern (CADPAT), first issued in 2002, and then with US work led by O'Neill which created Marine pattern (MARPAT), launched between 2002 and 2004.

The scale of camouflage patterns is related to their function. Large structures need larger patterns than individual soldiers to disrupt their shape. At the same time, large patterns are more effective from afar, while small scale patterns work better up close. Traditional single scale patterns work well in their optimal range from the observer, but an observer at other distances will not see the pattern optimally. Nature itself is very often fractal, where plants and rock formations exhibit similar patterns across several magnitudes of scale. The idea behind multi-scale patterns is both to mimic the self-similarity of nature, and also to offer scale invariant or so-called fractal camouflage that works at close range as well as at traditional combat range.

Animals such as the flounder have the ability to adapt their camouflage patterns to suit the background, and they do so extremely effectively, selecting patterns that match the spatial scales of the current background.

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