Nonlinear State Estimation in Observation Noise of Unknown Covariance.
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1975
- Accession Number
- ADA015603
Entities
People
- Daniel L. Alspach
- Louis L. Louis L. Scharf
Organizations
- Colorado State University