The Journal of the Astronautical Sciences

, Volume 58, Issue 3, pp 461–478 | Cite as

Considering Measurement Model Parameter Errors in Static and Dynamic Systems

  • Drew P. Woodbury
  • Manoranjan Majji
  • John L. Junkins


In static systems, state values are estimated using traditional least squares techniques based on a redundant set of measurements. Inaccuracies in measurement model parameter estimates can lead to significant errors in the state estimates. This paper describes a technique that considers these parameters in a modified least squares framework. It is also shown that this framework leads to the minimum variance solution. Both batch and sequential (recursive) least squares methods are described. One static system and one dynamic system are used as examples to show the benefits of the consider least squares methodology.


Kalman Filter State Estimate Error Covariance Bound Position Estimate Error Gravity Coefficient 
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Copyright information

© American Astronautical Society, Inc. 2011

Authors and Affiliations

  • Drew P. Woodbury
    • 1
  • Manoranjan Majji
    • 1
  • John L. Junkins
    • 1
  1. 1.Aerospace EngineeringTexas A&M University, TAMU 3141USA

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