An adaptive filter using scanning observation with application to a DPS (original) (raw)

In this paper an observation strategy is presented, based on the concept of scanning observability, for finite-dimensional linear systems with discrete observations. It aims to improve the convergence properties of the extended Kalman filter, regarded as an adaptive estimator of the state for the above class of systems. A significant application to a distributed parameter system is shown, related to the problem of determining a convenient trajectory for a moving sensor or a convenient scanning observation rule over a set of fixed sensors. The effectiveness of the resulting filter is analysed on the basis of several numerical experiments.