Subspace Processors for Physics‐Based Application
Dr James Candy · 2019
In this chapter, we develop a suite of case studies applying model-based identification (MBID) techniques to extract models for processing using both subspace and parametrically adaptive schemes. We start with a complex mechanical (structural) system applying subspace techniques followed by the development of a physics-based model for Kalman filtering in a scintillation system applying both identification approaches. Two MBID schemes using Bayesian particle filters (PF) are developed from the underlying phenomenology in order to identify/detect fission processes as well as modal functions propagating in a shallow ocean environment. Finally, we extract (estimate) chirp and frequency-shift key (FSK) signals from noisy data using the parametrically adaptive, unscented Kalman filter (UKF) approach.