skip to main content
10.1145/3366486.3366512acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
short-paper

Training-based Adaptive Channel Tracking for Correlated Underwater Acoustic Channels

Published: 13 February 2020 Publication History

Abstract

Multipath arrivals in many underwater acoustic channels are cross-correlated due to recent study. By exploiting the cross-correlation of mulipath arrivals, an efficient type of channel tracking for underwater acoustic channel is proposed by decomposing the cross-correlation into the channel principal components with low rank and the corresponding channel subspace. To track the channel, the channel principal components are modeled as an autoregressive (AR) process, and a Kalman filter tracks the channel components based on this AR model. The channel subspace is also tracked by recursive algorithms. However, these multi-procedure algorithms leave many undecided parameters which can affect the performance substantially. And the mismatch of the priori model is inevitable for underwater acoustic channels. In this paper, we present a training-based adaptive channel tracking algorithm. With the help of a short prior sequence of data, the parameters for the trackers are obtained through training. And an adaptive Kalman filter is used to correct the mismatch of the model which is also training-based. Performance of the proposed algorithms is demonstrated with real sea data. For the real sea data analyzed, the channel tracking accuracy is improved both in calm sea and rough sea.

References

[1]
Hirotugu Akaike. 1978. A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics 30, 1 (1978), 9--14.
[2]
C. R. Berger, S. Zhou, J. C. Preisig, and P. Willett. 2010. Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing. IEEE Transactions on Signal Processing 58, 3 (March 2010), 1708--1721. https://doi.org/10.1109/TSP.2009.2038424
[3]
Léon Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010. Springer, 177--186.
[4]
X. Chen, W. Li, Q. Lu, P. Willett, and Q. Zhang. 2018. Underwater Acoustic Channel Tracking by Multi-Bernoulli Filter. In 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO). 1--8. https://doi.org/10.1109/OCEANSKOBE.2018.8558861
[5]
Steven He Huang, Jenho Tsao, and T. C. Yang. 2014. Model-Based Signal Subspace Channel Tracking for Correlated Underwater Acoustic Communication Channels. IEEE Journal of Oceanic Engineering 39, 2 (2014), 343--356.
[6]
S H Huang, T C Yang, and Huang Chen-Fen. 2013. Multipath correlations in underwater acoustic communication channels. Journal of the Acoustical Society of America 133, 4 (2013), 2180--90.
[7]
S. H. Huang, T. C. Yang, and Jenho Tsao. 2015. Improving channel estimation for rapidly time-varying correlated underwater acoustic channels by tracking the signal subspace. Ad Hoc Networks 34, C (2015), 17--30.
[8]
R. E. Kalman. 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82, 1 (1960), 35--45.
[9]
R. Nadakuditi and J. C. Preisig. 2004. A Channel Subspace Post-Filtering Approach to Adaptive Least-Squares Estimation. Signal Processing IEEE Transactions on 52, 7 (2004), 1901--1914.
[10]
B. Widrow, J. McCool, and M. Ball. 1975. The complex LMS algorithm. Proc. IEEE 63, 4 (1975), 719--720.
[11]
Lin Chengyu Wu Benqing, Qian Liang. 2012. A Variable Forgetting Factor of Kalman Filter Channel Tracking. Information Technology 6 (2012), 87--91.
[12]
Bin Yang. 1995. Projection Approximation Subspace Tracking. 43, 1 (1995), 95--107.

Cited By

View all
  • (2021)Reinforcement Learning-Based Underwater Acoustic Channel Tracking for Correlated Time-Varying ChannelsOCEANS 2021: San Diego – Porto10.23919/OCEANS44145.2021.9705830(1-5)Online publication date: 20-Sep-2021
  • (2021)Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-Varying ChannelsIEEE Access10.1109/ACCESS.2021.30693369(50485-50495)Online publication date: 2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
WUWNet '19: Proceedings of the 14th International Conference on Underwater Networks & Systems
October 2019
210 pages
ISBN:9781450377409
DOI:10.1145/3366486
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Underwater acoustic channel
  2. channel tracking
  3. training-based

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

WUWNET'19

Acceptance Rates

Overall Acceptance Rate 84 of 180 submissions, 47%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)7
  • Downloads (Last 6 weeks)0
Reflects downloads up to 30 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Reinforcement Learning-Based Underwater Acoustic Channel Tracking for Correlated Time-Varying ChannelsOCEANS 2021: San Diego – Porto10.23919/OCEANS44145.2021.9705830(1-5)Online publication date: 20-Sep-2021
  • (2021)Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-Varying ChannelsIEEE Access10.1109/ACCESS.2021.30693369(50485-50495)Online publication date: 2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media