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.
Considering Measurement Model Parameter Errors in Static and Dynamic Systems
The Journal of the Astronautical Sciences ; 58 , 3 ; 461-478
2011-07-01
18 pages
Article (Journal)
Electronic Resource
English
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