Title :
Lossless cascade networks and stochastic estimation
Author_Institution :
Inf. Syst. Lab., Stanford Univ., CA, USA
Abstract :
The notion of matrices with generalized displacement structure is introduced. An efficient procedure for Cholesky factorization of nonstationary covariances with such structure is presented. An inverse scattering interpretation of this procedure relates it to lossless cascade models with p+q-1 parameters per layer where { p, q} denotes the displacement inertia of the covariance matrix. Matrices with displacement inertia are of particular interest: they have given rise to cascade models that are lossless two-ports, with a single parameter per layer. The author uses the cascade model to construct Levinson-type recursions for the prediction polynomials associated with structured nonstationary covariances
Keywords :
cascade networks; circuit theory; estimation theory; matrix algebra; multiport networks; Cholesky factorization; Levinson-type recursions; displacement inertia; generalized displacement structure; inverse scattering; lossless cascade networks; lossless two-ports; multiport networks; nonstationary covariances; prediction polynomials; stochastic estimation; Contracts; Covariance matrix; Integrated circuit modeling; Inverse problems; Lattices; Nonlinear filters; Speech synthesis; Stochastic processes; Transmission line matrix methods; Transmission lines;
Conference_Titel :
Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
Conference_Location :
Tampa, FL
DOI :
10.1109/CDC.1989.70066
