Treatment of Extended Kalman Filter Implementations for the Gyroless Star Tracker

Abstract

The literature since Apollo contains exhaustive material on attitude filtering, usually treating the problem of two sensors, a combination of state measuring and inertial devices. More recently, it has become popular for a sole attitude determination device to be considered. This is especially the case for a star tracker given its unbiased stellar measurement and recent improvements in optical sensor performance. The state device indirectly estimates the attitude rate using a known dynamic model. In estimation theory, two main attitude filtering approaches are classified, the additive and the multiplicative. Each refers to the nature of the quaternion update in the filter. In this article, these two techniques are implemented for the case of a sole star tracker, using simulated and real night sky image data. Both sets of results are presented and compared with each other, with a baseline established through a basic linear least square estimate. The state approach is more accurate and precise for measuring angular velocity than using the error-based filter. However, no discernible difference is observed between each technique for determining pointing. These results are important not only for sole device attitude determination systems, but also for space situational awareness object localisation, where attitude and rate estimate accuracy are highly important.

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

“…The quaternion estimation error is defined as an algebraic error by . is only a mathematical description [ 28 ], and the corrected quaternion is provided by: …”

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

“…Madyastha [ 28 ] proposed a Kalman filtering method based on attitude error states. The error state Kalman filter (ESKF) is designed for covariance estimation, which serves as a weight for the inertial odometry optimization process [ 29 , 30 , 31 ]. These results validate the ESKF’s effectiveness for error−state estimation, although a full−state estimation based on ESKF is not available yet.…”

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

“…There are numerous methods, including infrared tracking, laser tracking, sonar tracking, and other types of tracking, due to the development of associated technologies, the diversity of the types of tracked objects, and the complexity of motion [ 2 ]. Target tracking plays an essential role in many fields, such as vehicle monitoring [ 3 ], space attitude perception [ 4 ], and missile guidance [ 5 ]. Many literature studies focus on efficiently tracking high-speed maneuvering targets with fast flight speeds and complex trajectories [ 6 , 7 , 8 , 9 , 10 ].…”