Adaptive and robust experimental design for linear dynamical models using Kalman filter
This work addresses a specific issue in experimental design for dynamical systems, offering an incremental improvement by integrating existing techniques to better account for noise and parameter uncertainty.
The paper tackled the problem of experimental design for linear dynamical systems with both process and measurement noise, where existing methods often ignore process noise, by developing a robust methodology that combines Bayesian and adaptive approaches to handle uncertain model parameters, resulting in a framework that updates designs based on gathered measurements.
Current experimental design techniques for dynamical systems often only incorporate measurement noise, while dynamical systems also involve process noise. To construct experimental designs we need to quantify their information content. The Fisher information matrix is a popular tool to do so. Calculating the Fisher information matrix for linear dynamical systems with both process and measurement noise involves estimating the uncertain dynamical states using a Kalman filter. The Fisher information matrix, however, depends on the true but unknown model parameters. In this paper we combine two methods to solve this issue and develop a robust experimental design methodology. First, Bayesian experimental design averages the Fisher information matrix over a prior distribution of possible model parameter values. Second, adaptive experimental design allows for this information to be updated as measurements are being gathered. This updated information is then used to adapt the remainder of the design.