Estimation of signal subspace-constrained inputs to linear systems
Alex Fink, Andreas Spanias · 2011
Estimation of inputs to deterministic linear systems is of interest in applications from target tracking to sound resynthesis. Considering prior information about inputs, such as the time-limited nature of striking a musical instrument, estimates may be made to meet known constraints. This paper presents a method of estimating, based on noisy observations, inputs in terms of a basis expansion, where the inputs are known a priori to be constrained to a signal subspace. It is shown how input estimates may be obtained via least-squares estimation, including recursive algorithms. Simulation results are given to show the improvement of estimation where constraints are known. Additionally, application to sound resynthesis is presented.