TY - JOUR
T1 - Robust spacecraft angular rate estimation from vector observations using interlaced particle filtering
AU - Carmi, Avishy
AU - Oshman, Yaakov
N1 - Funding Information:
This research was supported by the Israel Science Foundation (Grant No. 1032/04), the Technion’s Asher Space Research Fund, and the Robert and Mildred Rosenthal Aerospace Engineering Research Fund. The authors wish to express their gratitude to Paolo Tortora of the university of Bologna, for providing his EKF computer code.
PY - 2007/1/1
Y1 - 2007/1/1
N2 - A novel algorithm is presented for the estimation of the spacecraft angular rate from vector observations. Belonging to the class of Monte Carlo sequential methods, the new estimator is a particle filter that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in inherently nonlinear systems. This paper develops the filter and its implementation in the case of a low Earth orbit spacecraft, acquiring noisy geomagnetic field measurements via a three-axis magnetometer. Because the effective measurement noise in this case is time correlated, a special procedure is developed to account for that correlation in the particle filter implementation. The new estimator copes with the absence of an exact inertia tensor by employing a secondary particle filter that computes a maximum-likelihood estimate of the tensor of inertia, thus avoiding the need to expand the primary filter's state. This renders the new estimator highly efficient and enables its implementation with a remarkably small number of particles. The results of a simulation study are presented, in which the new filter is compared to a recently presented conventional extended Kalman filter. The comparison demonstrates the viability and robustness of the new algorithm and its fast convergence rate.
AB - A novel algorithm is presented for the estimation of the spacecraft angular rate from vector observations. Belonging to the class of Monte Carlo sequential methods, the new estimator is a particle filter that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in inherently nonlinear systems. This paper develops the filter and its implementation in the case of a low Earth orbit spacecraft, acquiring noisy geomagnetic field measurements via a three-axis magnetometer. Because the effective measurement noise in this case is time correlated, a special procedure is developed to account for that correlation in the particle filter implementation. The new estimator copes with the absence of an exact inertia tensor by employing a secondary particle filter that computes a maximum-likelihood estimate of the tensor of inertia, thus avoiding the need to expand the primary filter's state. This renders the new estimator highly efficient and enables its implementation with a remarkably small number of particles. The results of a simulation study are presented, in which the new filter is compared to a recently presented conventional extended Kalman filter. The comparison demonstrates the viability and robustness of the new algorithm and its fast convergence rate.
UR - http://www.scopus.com/inward/record.url?scp=36849076778&partnerID=8YFLogxK
U2 - 10.2514/1.28932
DO - 10.2514/1.28932
M3 - Article
AN - SCOPUS:36849076778
SN - 0731-5090
VL - 30
SP - 1729
EP - 1741
JO - Journal of Guidance, Control, and Dynamics
JF - Journal of Guidance, Control, and Dynamics
IS - 6
ER -