A satellite collision avoidance system based on General Regression Neural Network

Continuous launching of new satellites and increasing numbers of space missions is making space a congested environment. Collision with space debris or other satellites is now a real problem for satellites with the problem more acute in highly trafficked orbits. Thus, mission operators and space agencies are in need of high accuracy collision avoidance systems for spacecrafts and satellites. This paper focuses on tackling the satellite collision problem by implementing a collision avoidance system using neural networks and relevant machine learning techniques. The primary model is based on General Regression Neural Network (GRNN) and the secondary models are based on Artificial Neural Networks (ANN), Random Forest Regression & Support Vector Regression techniques. The dataset used in this paper is collected from the ESA (European Space Agency) which contains the events of risk assessment or in other words, Conjunction Data Messages (CDM). The proposed collision avoidance system predicts the collision risk percentage between target (a satellite of interest) and chaser (space debris or another satellite) objects. The predicted risk enables the target to maneuver accordingly and ultimately avoid collision with the chaser object. The GRNN algorithm uses lazy learning which does not require iterative training and makes predictions based on stored parameters. The training data has been normalized before applying the algorithm as GRNN network is sensitive to high deviation among input features. However, the GRNN model predicts the risk of collision between the target & the chaser object with an MSE (Mean Squared Error) of 11% which means the model predicts the risk of collision with 89% accuracy and this 89% risk can give enough confidence factor to the concerned authority to take necessary evasive maneuvers. This is reliable enough and lower than other models' MSE to consolidate the fact that the GRNN model is best fit for our dataset.