What is the basic difference between a nonlinear kalman filter operation and a nonlinear dynamic data reconciliation algorithm? Is it only the ability to handle constraints, or is there some fundamental difference between the two?
The data reconciliation and Kalman filtering algorithms both solve the optimization problem by minimizing the MSE. Therefore, under the conditions claimed by Kalman, they are convertible. However, the conceptual steps are different. In applications, you may find hybrid structures with prefiltering and then reconciliation, visa versa, or even suggestions to use the reconciliation algorithm as an alternative to Kalman. But note that the Kalman filter is an elegant and widely recognized engineering solution. And that commonly generates a problem for other approaches to compete.
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