12 May 2010 Evolutionary optimization for tuning nonlinear airborne sightline controllers using an image-based quality metric
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Abstract
For long-range imaging or low signal-to-noise ratio environments sightline jitter is a primary source of image degradation. The conventional pointing & stabilization figure of merit is therefore the jitter RMS, with bearing friction often the largest contributor overall. Recent work has shown that pixel smear during camera integration can be reduced if adaptive friction compensation 'shapes' the jitter frequency content in addition to reducing the RMS value. This paper extends this work by automating the tuning process for the sightline control parameters by using a genetic algorithm. The GA fitness metric is the integral of the modulation transfer function due to any residual sightline jitter. It is shown that this fitness function is significantly better than the current root-mean-square figure of merit typically employed in stabilization loop design.
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David Anderson, "Evolutionary optimization for tuning nonlinear airborne sightline controllers using an image-based quality metric", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769618 (12 May 2010); doi: 10.1117/12.849524; https://doi.org/10.1117/12.849524
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