A counterpropagation neural network for determining target spacecraft orientation

Abstract

This paper describes a concept that integrates a counterpropagation neural network into a video-based vision system employed for automatic spacecraft docking. A brief overview of docking phases, the target orientation problem, and potential benefits resulting from an automated docking system is provided. Issues and challenges of automatic target recognition, as applied to automatic docking, are addressed. Following a review of the architecture, training, and desirable characteristics of the counterpropagation network, an approach for determining the relative orientation of a target spacecraft based on a counterpropagation net is presented.<>