Stochastic wind modeling and estimation for unmanned aircraft systems

Matthew Rhudy, Jason Gross, Yu Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


This paper presents a sensor fusion technique for wind estimation using two different stochastic models for wind speed: random walk and Gauss-Markov. The parameters for these models are formally derived from experimentally collected weather station data using an Allan deviation approach. Using these parameters, the two different stochastic wind models are then implemented within a nonlinear Kalman filtering approach to wind estimation and compared using two sets of unmanned aircraft flight data. This work showed that even though the Gauss-Markov model can more closely model the distribution of wind speed, only small differences are noted when comparing to the commonly implemented random walk model.

Original languageEnglish (US)
Title of host publicationAIAA Aviation 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Number of pages9
ISBN (Print)9781624105890
StatePublished - 2019
EventAIAA Aviation 2019 Forum - Dallas, United States
Duration: Jun 17 2019Jun 21 2019

Publication series

NameAIAA Aviation 2019 Forum


ConferenceAIAA Aviation 2019 Forum
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Aerospace Engineering


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