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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    Title :

    Considering Measurement Model Parameter Errors in Static and Dynamic Systems


    Contributors:

    Published in:

    Publication date :

    2011




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    Local classification TIB:    770/7040
    BKL:    55.60 Raumfahrttechnik





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