Bayesian Estimation With Distance Bounds

We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Sinc...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2012-12, Vol.19 (12), p.880-883
Main Authors: Zachariah, D., Skog, I., Jansson, M., Handel, P.
Format: Article
Language:English
Subjects:
Approximation methods
Bayesian estimation
Bayesian methods
Covariance matrix
distance
Estimation
Mean square error methods
positioning
Probability density function
tracking
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Since finding the conditional mean requires multidimensional integration, an approximate MMSE estimator is proposed. The performance of the proposed estimator is evaluated in a positioning problem. Finally, the application of the estimator in inequality constrained recursive filtering is illustrated by applying the estimator to a dead-reckoning problem. The MSE of the estimator is compared with two related posterior Cramér-Rao bounds.
ISSN:1070-9908
1558-2361
1558-2361
DOI:10.1109/LSP.2012.2224865