SS.stst.SMW: Steady State using the Woodbury matrix identity

In SSsimple: State Space Models

Description

Find steady state of system, i.e., locate when Kalman gain converges

Usage

SS.stst.SMW(F, H, Q, inv.R, P0, epsilon, verbosity=0)

Arguments

F	The state matrix. A scalar, or vector of length d, or a d x d matrix. When scalar, F is constant diagonal. When a vector, F is diagonal.
H	The measurement matrix. Must be n x d.
Q	The state variance. A scalar, or vector of length d, or a d x d matrix. When scalar, Q is constant diagonal. When a vector, Q is diagonal.
inv.R	The inverse of the measurement variance. A scalar, or vector of length n, or a n x n matrix. When scalar, inv.R is constant diagonal. When a vector, inv.R is diagonal.
P0	Initial a priori prediction error.
epsilon	A small scalar number.
verbosity	0, 1 or 2.

Details

Spiritually identical to SS.stst, except that the Woodbury identity is used for inversion. This method offers a computationally reduced means of finding the system steady state; however, this method must be supplied with the inverse of the measurement variance matrix, R – not R. Try comparing the example below with the evivalent example offered for SS.stst.

Value

A named list.

P.apri	A d x d matrix giving a priori prediction variance.
P.apos	A d x d matrix giving a posteriori prediction variance.

Examples

H <- matrix(1)

SS.stst.SMW(1, H, 1, 1, P0=10^5, epsilon=10^(-14), verbosity=1)

SSsimple documentation built on Dec. 7, 2019, 9:06 a.m.