Editing Observability (section)

== Static systems and general topological spaces ==

Observability may also be characterized for steady state systems (systems typically defined in terms of algebraic equations and inequalities), or more generally, for sets in <math>\mathbb{R}^n</math>.<ref>{{cite journal|doi=10.1016/0009-2509(81)85004-X|url=https://gregstanleyandassociates.com/CES-1981a-ObservabilityRedundancy.pdf|title=Observability and redundancy in process data estimation |year=1981 |last1=Stanley |first1=G. M. |last2=Mah |first2=R. S. H. |journal=Chemical Engineering Science |volume=36 |issue=2 |pages=259–272 |bibcode=1981ChEnS..36..259S }}</ref><ref>{{cite journal|doi=10.1016/0009-2509(81)80034-6|url=https://gregstanleyandassociates.com/CES-1981b-ObservabilityRedundancyProcessNetworks.pdf|title=Observability and redundancy classification in process networks |year=1981 |last1=Stanley |first1=G.M. |last2=Mah |first2=R.S.H. |journal=Chemical Engineering Science |volume=36 |issue=12 |pages=1941–1954 }}</ref> Just as observability criteria are used to predict the behavior of [[Kalman filter]]s or other observers in the dynamic system case,  observability criteria for sets in <math>\mathbb{R}^n</math> are used to predict the behavior of [[data validation and reconciliation|data reconciliation]] and other static estimators. In the nonlinear case,  observability can be characterized for individual variables, and also for local estimator behavior rather than just global behavior.