On the state space and dynamics selection in linear stochastic models: a spectral factorization approach
Provides a theoretical unification for state space and dynamics selection in linear stochastic systems, relevant to control and signal processing communities.
The paper studies the relation between two Riccati equations that parametrize the pole and zero structures of spectral factors in linear stochastic models, enabling construction of general spectral factors via zero- and pole-flipping on a reference factor.
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed analytic pole configuration. The classification of spectral factors then involves studying the solutions of a certain algebraic Riccati equation which parametrizes their zero structure. The pole structure of the spectral factors can be also parametrized in terms of solutions of another Riccati equation. We study the relation between the solution sets of these two Riccati equations and describe the construction of general spectral factors which involve both zero- and pole-flipping on an arbitrary reference spectral factor.