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Considering Measurement Model Parameter Errors in Static and Dynamic Systems

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Abstract

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.

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Authors and Affiliations

  1. Aerospace Engineering, Texas A&M University, TAMU 3141, USA

    Drew P. Woodbury (Ph.D. Student), Manoranjan Majji (Postdoctoral Assistant) & John L. Junkins (Distinguished Professor)

Authors
  1. Drew P. Woodbury
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  2. Manoranjan Majji
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  3. John L. Junkins
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Woodbury, D.P., Majji, M. & Junkins, J.L. Considering Measurement Model Parameter Errors in Static and Dynamic Systems. J of Astronaut Sci 58, 461–478 (2011). https://doi.org/10.1007/BF03321180

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  • DOI: https://doi.org/10.1007/BF03321180

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