Robust spacecraft angular rate estimation from vector observations using interlaced particle filtering

Avishy Carmi, Yaakov Oshman

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
Pages (from-to)1729-1741
Number of pages13
JournalJournal of Guidance, Control, and Dynamics
Issue number6
StatePublished - 1 Jan 2007
Externally publishedYes

ASJC Scopus subject areas

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


Dive into the research topics of 'Robust spacecraft angular rate estimation from vector observations using interlaced particle filtering'. Together they form a unique fingerprint.

Cite this