By Topic

A Sparse Kalman Filter with Application to Acoustic Communications Channel Estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Iltis, R.A. ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA

A Sparse Bayesian Kalman filter (SB-KF) is developed for channel estimation in underwater acoustic communications. The SB-KF algorithm is based on parallel Kalman filtering, with each filter updated under a soft numerosity constraint. The soft constraint forces the one-step prediction of the channel to have fixed numerosity. The Bayesian framework yields both sparse channel estimates, and an estimate of the channel order (numerosity). Application of the SB-KF to an acoustic modem based on Walsh/m-sequence signaling is considered. A hybrid analysis/simulation approach is used to compute symbol error rates (SERs), which show that the sparse Bayesian algorithm significantly outperforms an unconstrained Kalman channel estimator

Published in:

OCEANS 2006

Date of Conference:

18-21 Sept. 2006