Compensation of position offset of acoustic transducers using compressive sensing concept

被引:0
作者
Murgan, Irina [1 ]
Digulescu, Angela [1 ,2 ]
Candel, Ion [1 ]
Ioana, Cornel [1 ]
机构
[1] Univ Grenoble Alpes, GIPSA Lab, St Martin Dheres, France
[2] Mil Tech Acad, Bucharest, Romania
来源
OCEANS 2016 MTS/IEEE MONTEREY | 2016年
关键词
compressive sensing; warping transform; time of arrival estimation; matched filter;
D O I
10.1109/OCEANS.2016.7761083
中图分类号
P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
This paper presents a new technique for acoustic transducers position offset compensation, based on compressive sensing reconstruction in the warped domain. In underwater acoustics, the transducers relative position is important for applications involving direction of arrival estimation, localization or source detection. When the transmitter-receiver transducers configuration is inappropriate, the received signal's informational content is not the same as the one of the emitted signal. In the case of applications such as underwater objects tracking, the experimental setup constraints and the water flow operational conditions lead to the perturbation of the emitted and received signals propagation. We use compressive sensing reconstruction of the received signal, in the warped domain, in order to recover its missing spectral information due to wave's propagation. Tests were conducted in a reduced scale experimental facility, in order to prove the interest of using the signal compressive sensing recovery for the signal's time of arrival estimation and to quantify the improvement introduced by this signal processing method. The results show that the time of arrival estimation can be considerably improved after the received signal's samples recovery, with the matched filter response improvement.
引用
收藏
页数:4
相关论文
共 50 条
  • [41] Video Compressive Sensing Using Gaussian Mixture Models
    Yang, Jianbo
    Yuan, Xin
    Liao, Xuejun
    Llull, Patrick
    Brady, David J.
    Sapiro, Guillermo
    Carin, Lawrence
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (11) : 4863 - 4878
  • [42] FAST BAYESIAN COMPRESSIVE SENSING USING LAPLACE PRIORS
    Babacan, S. Derin
    Molina, Rafael
    Katsaggelos, Aggelos K.
    2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, : 2873 - +
  • [43] Distributed Bayesian Compressive Sensing Using Gibbs Sampler
    Ai, Hua
    Lu, Yang
    Guo, Wenbin
    2012 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP 2012), 2012,
  • [44] Adaptive compressive sensing using optimized projection matrix
    Peng, Ya
    Song, Xiao Qin
    Zhu, Yong Gang
    COMPUTING, CONTROL, INFORMATION AND EDUCATION ENGINEERING, 2015, : 781 - 785
  • [45] Video coding using compressive sensing for wireless communications
    Li, Chengbo
    Jiang, Hong
    Wilford, Paul
    Zhang, Yin
    2011 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2011, : 2077 - 2082
  • [46] Compressive sensing with sparse domain division using probability
    Tian, Yumin
    Song, Jun
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2013, 40 (06): : 52 - 57
  • [47] FAST ENVIRONMENT MATTING EXTRACTION USING COMPRESSIVE SENSING
    Duan, Qi
    Cai, Jianfei
    Zheng, Jianmin
    Lin, Weisi
    2011 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2011,
  • [48] COMPRESSIVE RF SENSING USING A PHYSICAL SOURCE OF ENTROPY
    Rogers, Daniel J.
    Elkis, Radmil
    Chin, Sang
    Wayne, Michael A.
    2011 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2011, : 609 - 612
  • [49] Object Tracking in Video Signals using Compressive Sensing
    Kracunov, Marijana
    Bastrica, Milica
    Tesovic, Jovana
    2019 8TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2019, : 294 - 297
  • [50] Image encryption using sparse coding and compressive sensing
    R. Ponuma
    R. Amutha
    Multidimensional Systems and Signal Processing, 2019, 30 : 1895 - 1909