Improving attitude determination and control of resource-constrained CubeSats using unscented Kalman filtering

Abstract

CubeSats are a specific subset of nanosatellites, and their common form factor and canisterized deployers have made it possible to undertake higher risk, lower cost missions that can supplement the current generation of large, monolithic, expensive satellites. Our objective in this thesis is to improve attitude estimation on CubeSats using Unscented Kalman filters. CubeSats have evolved from their relatively low complexity and low computational power beginnings. This progression motivates us to revisit attitude determination estimation approaches commonly used for CubeSats, and to implement an alternative Kalman filtering method. Our goal is to improve the current state of the art in attitude estimation on previous MIT Space Systems Laboratory CubeSats by at least two orders of magnitude from about 1-5* attitude knowledge error down to 0.050 or better. This improvement benefits applications that require precise pointing, such as imaging and active tracking of specific targets, laser communications, and coordinated activity and observations among multiple CubeSats. We were able to achieve better than our pointing error goal of 0.05', and found that the proposed Unscented Kalman filter performed significantly better at high angular rate estimation than the Extended Kalman filter (already implemented on some CubeSats). The quaternion estimates were converted to Euler angles to improve ease of interpretation. For the majority of the missions, the mean total Euler angle estimation error improvement ranged from 83% - 98% with error variance decreased by as much as 98%. One implementation had more than a two order of magnitude improvement, to achieve 0.01* mean error, better than the desired pointing accuracy. We present a detailed assessment of these estimation errors, along with changes in quaternion error that accompany varying the unscented filter parameters.