TY - GEN

T1 - Adaptive estimation of spacecraft attitude from vector observations using a quaternion particle filter

AU - Oshman, Yaakov

AU - Carmi, Avishy

PY - 2006/5/22

Y1 - 2006/5/22

N2 - This paper presents an extension of the recently presented genetic algorithm-embedded quaternion particle filter (GA-QPF). Belonging to the class of Monte Carlo sequential methods, the GA-QPF is an estimator that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in the inherently nonlinear attitude estimation problem. The spacecraft attitude is represented via the quaternion of rotation, and a genetic algorithm is used to estimate the gyro biases, allowing to estimate just the quaternion via the particle filter. An adaptive version of the GA-QPF is presented herein, that extends the applicability of this filter to problems with highly uncertain measurement noise distributions. The adaptive algorithm estimates the measurement noise distribution on the fly, along with the estimation of the spacecraft attitude and gyro biases. A simulation study is used to demonstrate the performance of the adaptive algorithm using real data obtained from the Technion's TechSAT satellite, whose three-axis magnetometer's data is non-Gaussian. The simulation, which compares the performance of the filter to two alternative algorithms that are aware of the true statistical nature of the measurement noise, demonstrates the viability of the new algorithm.

AB - This paper presents an extension of the recently presented genetic algorithm-embedded quaternion particle filter (GA-QPF). Belonging to the class of Monte Carlo sequential methods, the GA-QPF is an estimator that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in the inherently nonlinear attitude estimation problem. The spacecraft attitude is represented via the quaternion of rotation, and a genetic algorithm is used to estimate the gyro biases, allowing to estimate just the quaternion via the particle filter. An adaptive version of the GA-QPF is presented herein, that extends the applicability of this filter to problems with highly uncertain measurement noise distributions. The adaptive algorithm estimates the measurement noise distribution on the fly, along with the estimation of the spacecraft attitude and gyro biases. A simulation study is used to demonstrate the performance of the adaptive algorithm using real data obtained from the Technion's TechSAT satellite, whose three-axis magnetometer's data is non-Gaussian. The simulation, which compares the performance of the filter to two alternative algorithms that are aware of the true statistical nature of the measurement noise, demonstrates the viability of the new algorithm.

UR - http://www.scopus.com/inward/record.url?scp=33646542970&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33646542970

SN - 0877035261

SN - 9780877035268

T3 - Advances in the Astronautical Sciences

SP - 229

EP - 245

BT - Malcolm D. Shuster Astronautics Symposium - Advances in the Astronautical Sciences - Proceedings of the University at Buffalo, State University of New York/AAS Malcolm D. Shuster Astronautics Symp.

T2 - Malcolm D. Shuster Astronautics Symposium

Y2 - 12 June 2005 through 15 June 2005

ER -