Design of an Extended Interacting Multiple Models Adaptive Estimator for Attitude Determination of a Stereoimagery Satellite

Abstract

We will design an extended interacting multiple models adaptive estimator (EIMMAE) for attitude determination of a stereoimagery satellite. This algorithm is based on interacting multiple models (IMM) extended kalman filters (EKF) using star sensor and gyroscope data. In this method, the motion of satellite during stereoimaging manoeuvres is partitioned into two different modes: "manoeuvring motion" mode and "uniform motion" mode. The proposed algorithm will select the suitable Kalman filter structure to estimate gyro errors accurately in order to maintain the peak attitude estimation error less enough at the beginning of manoeuvres while the satellite is in "manoeuvring motion" mode and then will select the suitable star sensor measurement noise level at the end of manoeuvres while the satellite is in "uniform motion" mode to reduce attitude estimation errors. It will be shown that using the proposed algorithm, the attitude estimation accuracy of stereoimagery satellite will be increased considerably. The effectiveness of the proposed algorithm will be examined and compared with the previous proposed methods through numerical simulations.

Attitude Determination Algorithms through Accelerometers, GNSS Sensors, and Gravity Vector Estimator

“…After defining an orthonormal base in the two systems of axes, the expression to determine the DCM is indicated in (20), where i B , j B , k B is a 3 × 3 square matrix composed of orthonormal vectors in the body triad, i NED , j NED , k NED expresses the same concept in the NED triad, and DCM B,NED is the director cosine matrix that transforms the NED triad into a body triad. The DCM matrix can be solved from (20) as it is shown in (21).…”

“…The DCM matrix can be solved from (20) as it is shown in (21). Note that employing an orthonormal base simplifies the calculation of the inverse matrix as it is the transposed matrix.…”

“…The results of these algorithms are compared for different types of measurement faults in different estimation scenarios, and recommendations about their applications are given. Another example is exposed in [20]. This algorithm is based on interacting multiple models (IMM) extended Kalman filters (EKF) using a star sensor and gyroscope data.…”

Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter

“…There are many kinds of vector observation sensors, such as star sensors, sun sensors, earth sensors, horizon sensors, magnetometers, and so on. 22,23 All of them have the same simplified model, 8 which is given bỹ…”

“…There are many kinds of vector observation sensors, such as star sensors, sun sensors, earth sensors, horizon sensors, magnetometers, and so on. 22,23 All of them have the same simplified model, 8 which is given by where rk+1 denotes the (k+1)th reference vector, and b~k+1 denotes the corresponding observation vector, δbk+1 is a zero-mean Gaussian white-noise process with 8 where μk+1 is the angular standard deviation.…”