Paper Title: Joint and Dual Estimation of States and Parameters with Extended and Unscented Kalman Filters

Recent Developments in Structural Health Monitoring and Assessment–Opportunities and Challenges: Bridges, Buildings and Other Infrastructures

Subhamoy Sen, Neha Aswal, & Baidurya Bhattacharya

Bayesian filtering-based Structural Health Monitoring (SHM) approaches predominantly employed Extended and Unscented Kalman filter variants (EKF and UKF) for joint estimation of states and parameters. In these approaches, a set of parameters denoting location-based system health indices are either appended in the response state vector of the system and estimated jointly as augmented states or estimated in parallel to the response states employing separate filters through dual estimation approach. This chapter discusses the relative benefits and constraints of the joint and dual estimation approaches specific for SHM problems when dealt with EKF and UKF environment. A comparative study is undertaken in this regard to arrive at an understanding about the relative benefits and applicability of the approaches for SHM. All the approaches are investigated on a numerical eight degrees of freedom mass-spring-damper system in which damage is simulated by numerically reducing stiffness value of a linear spring. The vibration response under Gaussian white noise excitation has been adopted as the measurement. The pros and cons of all the considered approaches are discussed in terms of accuracy, precision, promptness, and computation time.