Advanced sun-sensor processing and design for super-resolution performance

The performance of conventional and parametric super-resolution algorithms for estimating sun position in a spacecraft sun-sensor was analyzed. Widely employed in other applications, parametric algorithms were examined to evaluate increase in system performance without affecting the cost of the sensor system. Using a simplified model of detector illumination simulations provided quantitative comparisons of algorithm performance. Simple sensor re-design was examined by using genetic algorithms as a heuristic to optimize the illumination pattern for a single axis digital sun-sensor. Findings show that, multiple narrow peak patterns provide subpixel accuracy in resolving the sun-angle. The optimal illumination pattern can be implemented by fabricating a replacement aperture mask for the sensor and this change can be made at a minimal cost. The super-resolution algorithms were tested with a component noise model and image degradation due to Earth albedo effects were examined. Parametric algorithms display very good performance throughout the test regime. The improvements are substantial enough to validate this approach worthy of future study.