Bayesian Estimation With Distance Bounds
Abstract
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 deadreckoning problem. The MSE of the estimator is compared with two related posterior CramérRao bounds.
 Publication:

IEEE Signal Processing Letters
 Pub Date:
 December 2012
 DOI:
 10.1109/LSP.2012.2224865
 arXiv:
 arXiv:1210.3516
 Bibcode:
 2012ISPL...19..880Z
 Keywords:

 Mathematics  Statistics Theory
 EPrint:
 4 pages, 5 figures