Satellite position and attitude estimation using an infrared earth sensor

Abstract

Micro/nano-satellites are constrained by payload, size, and cost, making enhancements in design flexibility crucial, particularly for orbit and attitude determination systems. An effective strategy is the innovative utilization of existing sensors. This study investigates a novel application of Earth sensors for autonomous position and attitude determination enabling them as a viable option for autonomous navigation in low-cost micro/nano-satellites through computer vision technology. To address large resolution differences and distortions between sensed infrared images and reference maps, this paper presents a coarse-to-fine matching framework that integrates deep learning features and area-based registrations. First, CNN-based registration and optimization techniques are utilized for coarse registration. A modified VGG16 (M-VGG) and a multiscale pyramid information fusion (MPIF) module are employed to extract and describe deep abstract features, enhancing robustness. Subsequently, edge-based extended phase correlation is employed for fine registration. Finally, the ultimate position and attitude parameters are determined by integrating the results from coarse and fine registration. The effectiveness of the proposed scheme is validated using synthetic images. An analysis of 342 images demonstrates sub-pixel registration accuracy, with a root mean square error (RMSE) of 1.9322 km for position and 0.1185° for attitude.