Autoregressive model of an underwater acoustic channel in the frequency domain

被引:12
作者
Liu Biao [1 ,2 ,3 ]
Jia Ning [1 ,2 ,3 ]
Huang Jianchun [1 ,2 ,3 ]
Guo Shengming [1 ,2 ,3 ]
Xiao Dong [1 ,2 ]
Ma Li [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Inst Acoust, Beijing, Peoples R China
[2] Chinese Acad Sci, Key Lab Underwater Acoust Environm, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
关键词
UWA channel model; Channel frequency response; Autoregressive model; Pole parameterstatistical distribution;
D O I
10.1016/j.apacoust.2021.108397
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
The underwater acoustic (UWA) channel model based on the multipath delay-amplitude or the angle of departure (AoD)-angle of arrival (AoA) require many parameters to describe a broadband underwater acoustic (UWA) channel. In this paper, an autoregressive (AR) model is developed to describe the channel in the frequency domain with few parameters. The AR model used here is with an order P, where P is also related to the number of main multipath clusters. Hence, only P + 1 parameters, which consist of the variance of the complex Gaussian white noise (CGWN) and the P pole values of the AR model, are required to describe UWA channel. Each pole value is corresponded to a multipath cluster in the channel, where its phase is correlated with the average multipath delay, and its absolute value is related to the multipath energy. A comprehensive analysis was performed to characterize the statistical distribution of each pole value and the CGWN variance. The results showed that the absolute value and phase of each pole satisfied the Weibull and gamma distributions, respectively, and the variance obeyed a lognormal distribution. Simulation channels were generated with different distributions of the AR model parameters, and the root mean square (RMS) delay obeyed a similar cumulative distribution function as the measured channel. (C) 2021 Elsevier Ltd. All rights reserved.
引用
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页数:9
相关论文
共 26 条
[2]  
[Anonymous], 2009, OCEANS 2009 EUROPE
[3]   Shallow Water Acoustic Channel Modeling Based on Analytical Second Order Statistics for Moving Transmitter/Receiver [J].
Baktash, Ebrahim ;
Dehghani, Mohammad Javad ;
Nasab, Mohammad Reza Farjadi ;
Karimi, Mahmood .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (10) :2533-2545
[4]  
Borowski Brian, 2009, OCEANS 2009, DOI [10.23919/OCEANS.2009.5422360, DOI 10.23919/OCEANS.2009.5422360]
[5]  
Box GE, 2015, TIME SERIES ANAL FOR, V37, P19
[6]   Deep transfer learning for underwater direction of arrival using one vector sensora) [J].
Cao, Huaigang ;
Wang, Wenbo ;
Su, Lin ;
Ni, Haiyan ;
Gerstoft, Peter ;
Ren, Qunyan ;
Ma, Li .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2021, 149 (03) :1699-1711
[7]   Autoregressive Modeling of Mobile Radio Propagation Channel in Building Ruins [J].
Chen, Ling ;
Loschonsky, Marc ;
Reindl, Leonhard M. .
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2012, 60 (05) :1478-1489
[8]   Environment-aware communication channel quality prediction for underwater acoustic transmissions: A machine learning method [J].
Chen, Yougan ;
Yu, Weijian ;
Sun, Xiang ;
Wan, Lei ;
Tao, Yi ;
Xu, Xiaomei .
APPLIED ACOUSTICS, 2021, 181
[9]   Multivariate Autoregressive Spectrogram Modeling for Noisy Speech Recognition [J].
Ganapathy, Sriram .
IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (09) :1373-1377
[10]   A Nonisovelocity Geometry-Based Underwater Acoustic Channel Model [J].
Naderi, Meisam ;
Zajic, Alenka G. ;
Patzold, Matthias .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (04) :2864-2879