Title :
Cramer-Rao bounds for discrete-time nonlinear filtering problems
Author :
Doerschuk, Peter C.
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
fDate :
8/1/1995 12:00:00 AM
Abstract :
In this note, a Cramer-Rao bound for the mean squared error that can be achieved with nonlinear observations of a nonlinear pth order autoregressive (AR) process where both the process and observation noise covariances can be state dependent is presented. The major limitation is that the AR process must be driven by an additive white Gaussian noise process that has a full-rank covariance. A numerical example demonstrating the tightness of the bound for a particular problem is included
Keywords :
Gaussian noise; autoregressive processes; filtering theory; nonlinear filters; state estimation; Cramer-Rao bounds; additive white Gaussian noise process; discrete-time nonlinear filtering problems; full-rank covariance; mean squared error; nonlinear observations; nonlinear pth order autoregressive process; state dependent noise covariances; Additive noise; Additive white noise; Covariance matrix; Difference equations; Filtering; Gaussian noise; Parameter estimation; Probability density function; State estimation; Stochastic resonance;
Journal_Title :
Automatic Control, IEEE Transactions on
