Adaptive particle filtering for spacecraft attitude estimation from vector observations

Avishy Carmi, Yaakov Oshman

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

An extension is presented to the recently introduced genetic algorithm-embedded quaternion particle filter. Belonging to the class of Monte Carlo sequential methods, the genetic algorithm-embedded quaternion particle filter 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 one to estimate just the quaternion via the particle filter. An adaptive version of the genetic algorithm-embedded quaternion particle filter 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 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 are non-Gaussian. The simulation, which compares the performance of the filter to the nonadaptive genetic algorithm-embedded quaternion particle filter, demonstrates the viability of the new algorithm.

Original languageEnglish
Pages (from-to)232-241
Number of pages10
JournalJournal of Guidance, Control, and Dynamics
Volume32
Issue number1
DOIs
StatePublished - 1 Jan 2009
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Space and Planetary Science
  • Electrical and Electronic Engineering
  • Applied Mathematics

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