Amber Yang, Stanford University
Orbiting satellites and spacecraft in low earth orbit are subject to the collision dangers of more than 500,000 pieces of space debris, which can have an even greater impact on space vehicles if it is not tracked beforehand to allow the spacecraft to maneuver away from collision zones. However, current covariance-driven tracking tactics are vulnerable to orbital variations of space debris clouds, which orbit collectively, due to constantly changing astrodynamics subject to nonlinear celestial disturbances. In Amber Yang’s research, the Iterative Closest Point (ICP) algorithm is applied to register the space debris clouds from two successive motion scans as two point clouds for geometric alignment, which provides kinematic patterns of space debris clouds to train an artificial neural networks (ANN) system. The machine-learning backpropagation algorithm performs pattern recognition using an ANN to predict dynamic changes of the ICP kinematic patterns for accurate point-cloud tracking. Yang discusses how MATLAB® provides a cohesive environment for training and testing innovative applications to artificial intelligence.