Rao-Blackwellization for adaptive gaussian sum nonlinear model propagation

Sean R. Semper, John L. Crassidis, Jemin George, Siddharth Mukherjee, Puneet Singla

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Amarginalized adaptive Gaussian sum propagation was proposed. With this new method, the linear portion of the state-space model is propagated using the linear Kalman filter and the nonlinear portion is propagated using the unscented Kalman filter for each Gaussian component. Reducing the linear portion of the state-space model to linear propagation equations places the computational efforts on the nonlinear equations. Simulation results involving a parachute model indicate very similar results are obtained using the marginalized approach versus the non-marginalized approach. The computational burden using the marginalized approach may be higher or lower than the nonmarginalized approach, which is problem dependent.

Original languageEnglish (US)
Pages (from-to)1290-1295
Number of pages6
JournalJournal of Guidance, Control, and Dynamics
Volume38
Issue number7
DOIs
StatePublished - 2015

All Science Journal Classification (ASJC) codes

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

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