Proportionate Kalman Filter for Model-Based Channel Tracking in Underwater Acoustic Communications-Reference-Cited by-同舟云学术

Proportionate Kalman Filter for Model-Based Channel Tracking in Underwater Acoustic Communications

Published:2021-09-20 Issue: Volume: Page:
ISSN:
Container-title:OCEANS 2021: San Diego – Porto
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Author:

Wang Yuxing¹,Cao Hongli¹,Tao Jun¹,Yang Le²,Qiao Yongjie³

Affiliation:

1. Southeast University,Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education, School of Information Science and Engineering,Nanjing,China,210096

2. University of Canterbury,Department of ECE,Christchurch,New Zealand,8020

3. Pengcheng Lab,Shenzhen,China,518000

Funder

National Natural Science Foundation of China

State Key Laboratory of Acoustics

Chinese Academy of Sciences

Harbin Engineering University

Six Talent Peaks Project in Jiangsu Province

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9705657/9705658/09705832.pdf?arnumber=9705832

Reference14 articles.

1. Application of State-Space Methods to Navigation Problems

2. Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

3. A Proportionate Recursive Least Squares Algorithm and Its Performance Analysis

4. Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

5. Estimation with Applications to Tracking and Navigation

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Adaptive MP Algorithm for Underwater Acoustic Channel Estimation Based on Compressed Sensing;IEEE Access;2023

2. Outlier Robust Kalman Filter based Channel Tracking for Single-Carrier Underwater Acoustic Communications;OCEANS 2022, Hampton Roads;2022-10-17