Abstract
This article addresses the observability analysis and the optimal design of observation parameters in the presence of noisy measurements and parametric uncertainties. The notion of almost <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -observability is introduced and a systematic procedure to assess its satisfaction for a given system with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> known measurement noise statistics and parameter discrepancy is sketched. Moreover, the concept of observation-target quantities is introduced to analyze the precision with which specific chosen expressions of the state and the parameters can be reconstructed. The overall framework is exposed and validated through an illustrative example.
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