Lasers learn to accurately spot space junk

Phys.org  December 24, 2019
Scientists have developed space junk identification systems, but it has proven tricky to pinpoint the swift, small specks of space litter. Researchers in China trained a back propagation neural network to recognize space debris using two correcting algorithms. The Genetic Algorithm and Levenberg-Marquardt optimized the neural network’s thresholds for recognition of space debris, ensuring the network wasn’t too sensitive and could be trained on localized areas of space. The team demonstrated the improved accuracy by testing against three traditional methods. The observation data of 95 stars was used to solve the algorithm coefficients from each method, and the accuracy of detecting 22 other stars was assessed. The new pointing correction algorithms proved the most accurate, as well as easy to operate with good real-time performance…read more. Open Access TECHNICAL ARTICLE

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