Indika SSN Gamage
Detecting (localization and classification) space objects in close proximity is a problem arising from capturing, servicing, and other proximity operations. The localization result can aid relative navigation. The classification result is crucial for situation assessment and high-level control, planning, and decision making. In this paper, a method for detecting multiple 1U, 2U, 3U, and 6U CubeSats based on the faster region-based convolutional neural network (Faster R-CNN) is proposed. CubeSats detection model is developed using Web-searched images. A coarse single-point attitude estimation method is proposed utilizing the centroids of the bounding boxes surrounding the CubeSats in the image. The centroids define the line-of-sight (LOS) vectors to the detected CubeSats in the camera frame, and the LOS vectors in the reference frame are assumed to be obtained from global positioning system (GPS). The three-axis attitude is determined from the vector observations by solving Wahba’s problem. The attitude estimation concept is tested on simulated scenarios using Autodesk Maya.
English 
Spanish 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi