Unscented Kalman filter-based method for spacecraft navigation using resident space objects

Abstract

The number of resident space objects (RSOs) in orbit has increased dramatically within the last 10 years. While RSOs pose a serious challenge to our continued use of space, these objects also provide an opportunity to improve on-orbit state estimation by detecting and identifying these objects using star trackers. While star trackers are currently used to determine the orientation of their host spacecraft, their utility could be expanded to both position and orientation estimation by harnessing RSO detections. In order for RSO-based optical navigation to be commercially viable, a reliable filter covariance estimate is required. This paper introduces an Unscented Kalman Filter (UKF) for estimating an observing spacecraft's position and attitude based on RSO observations. To ensure that this filter is reliable, a new assessment technique is introduced where we compare the moving standard deviation and mean of the filter's estimate error with the predicted error based on the filter's covariance estimate. We demonstrate the utility of this assessment method by comparing the covariance trusts for our UKF and a previously developed Extended Kalman Filter.

Resilient Multi-Source Integrated Navigation Method for Aerospace Vehicles Based on On-Line Evaluation of Redundant Information

“…In Equation (11), The measurement information in the measurement equation consists of the difference in velocity, position, and attitude between the INS output and other sensors output.…”

“…From Figure 3, at moment , the + 1 moment state quantities | can be predicted with Equation (10), and in combination with Equation (11), the predicted value of the quantity measurement at + 1 time | can be calculated. Taking Equation (10) into Equation (11), the actual output of the measurement at moment is obtained as…”

“…Public information shows that the aerospace vehicle navigation system adopts multi-source redundancy configuration scheme to meet its system fault tolerance requirements. Therefore, based on inertial navigation system, according to the environmental characteristics of different flight stages, different types of auxiliary navigation sensors [8] are used to improve the reliability of the navigation system has become the consensus of researchers, such as satellite navigation system [9], celestial navigation system [10], atmospheric altitude measurement system, synthetic aperture radar, and so on [11]. The key of aerospace vehicle navigation system to meet its high-precision and reliable measurement requirements lies in: How to fuse multi-source navigational information that has significant spatiotemporal heterogeneity.…”

Maximum correentropy-based robust Square-root Cubature Kalman Filter for vehicular cooperative navigation

“…Therefore, it can be easily applied to the estimation of the state of a nonlinear system 11 , 12 . Driedger et al 13 found that EKF and UKF were applied to evaluate the feasibility of optical navigation based on resident space objects, and experiments showed that UKF was more reliable than EKF. Deori et al 14 used EKF and UKF to design and test the benchmark cart pendulum system and the underactuated offshore boom crane system, and the results showed that the accuracy of UKF was higher than that of EKF.…”