Inequality constrained parameter estimation using filtering approaches

Yang, Xiongtan; Huang, Biao; Prasad, Vinay

doi:10.1016/j.ces.2013.11.038

Cited by 4 publications

(1 citation statement)

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“…The ability to constrain a system often has a number of advantages that can play an important role in state and parameter estimation: they can be used to enforce physicality of modeled systems (non-negativity of physical quantities, for example); relatedly they can be used to ensure that computational models are employed only within state and parameter regimes where the model is well-posed; and finally the application of constraints may provide robustness to outlier data. Resulting improvements in algorithmic efficiency and performance, by means of enforcing constraints, has been demonstrated in the recent literature in a diverse set of fields, including process control [1], biomechanics [2], cell energy metabolism [3], medical imaging [4], engine health estimation [5], weather forecasting [6], chemical engineering [7], and hydrology [8]. Within the Kalman filtering literature the need to incorporate constraints is widely recognized and has been addressed in a systematic fashion by viewing Kalman filtering from the perspective of optimization.…”

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confidence: 99%

Ensemble Kalman methods with constraints

et al. 2019

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Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it.

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