Nonlinear State Estimation in Observation Noise of Unknown Covariance.

摘要

The problem of estimating the state of a stationary Gauss-Markov sequence observed in uncorrelated Gaussian noise of constant, but unknown, covariance R is considered. An inverted Wishart prior is assigned to the prior innovations covariance M, which is unknown by virtue of the uncertainty in R. The resulting nonlinear state estimator involves a canonical integral which can be approximated to yield an attractive parallel filtering structure. The structure can be used, also, to approximate the maximum a posteriori (MAP) estimate of the innovations covariance.