Sparse Representation Theory and Application in Underwater Acoustic Signal Processing

FENG Xi-an; KOU Si-wei; TAN Wei-jie; BI Yang

doi:10.12263/DZXB.20200492

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Sparse Representation Theory and Application in Underwater Acoustic Signal Processing

SURVEYS AND REVIEWS | 更新时间：2025-12-08

- Sparse Representation Theory and Application in Underwater Acoustic Signal Processing
- ACTA ELECTRONICA SINICA Vol. 49, Issue 9, Pages: 1840-1851(2021)
- 作者机构：
  
  1.西北工业大学航海学院，陕西西安 710072
  2.贵州大学计算机科学与技术学院，贵州贵阳 550025
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200492
  CLC： TB566
- Received：22 May 2020，
  
  Revised：2021-03-17，
  
  Published：25 September 2021
- 稿件说明：
移动端阅览
冯西安,寇思玮,谭伟杰等.水声信号处理中的稀疏表示理论及应用[J].电子学报,2021,49(09):1840-1851.

FENG Xi-an,KOU Si-wei,TAN Wei-jie,et al.Sparse Representation Theory and Application in Underwater Acoustic Signal Processing[J].ACTA ELECTRONICA SINICA,2021,49(09):1840-1851.
冯西安,寇思玮,谭伟杰等.水声信号处理中的稀疏表示理论及应用[J].电子学报,2021,49(09):1840-1851. DOI： 10.12263/DZXB.20200492.

FENG Xi-an,KOU Si-wei,TAN Wei-jie,et al.Sparse Representation Theory and Application in Underwater Acoustic Signal Processing[J].ACTA ELECTRONICA SINICA,2021,49(09):1840-1851. DOI： 10.12263/DZXB.20200492.

摘要

稀疏表示研究信号简洁表示与重构的本质问题，能够更好地揭示、分辨和提取信号中所蕴含的信息特征，在水声信号处理的许多应用方面都显示了巨大的优势和潜力.本文综述了水声信号处理中的稀疏表示理论及有关应用问题.首先介绍了稀疏表示模型和典型的稀疏分解算法；然后，研究了自适应过完备字典设计、离网格处理等稀疏表示的关键问题；接着，探索了稀疏表示理论在水下信号处理中的一些重要应用，包括高分辨波达方向（Direction Of Arrival

DOA）估计、水下体目标微多普勒特征提取、运动目标角度-多普勒声成像、水声信号压缩感知与重构；最后，指出稀疏表示理论在水声信号处理中的发展趋势.进行了必要的计算机仿真，提取了水下目标时、频、空域多维度信息特征，并实现了两类典型通信信号的有效压缩和精确重构.

Abstract

Sparse representation is a theory to study the essential problem of signal concise representation and precise recovery. It can better reveal

distinguish and extract the characteristic information contained in underwater acoustic signals，so that it has great advantages and potential in many applications of underwater acoustic signal processing. In this paper

the sparse representation theory and its application in underwater acoustic signal processing are reviewed. Firstly

the sparse representation model and typical sparse decomposition algorithms are introduced. Then

the key problems of sparse representation

such as adaptive over-complete dictionary design and off-grid processing and so on

are studied. Thirdly

some important applications of sparse representation theory in underwater signal processing are explored

which include high-resolution DOA estimation

micro-Doppler feature extraction of underwater target

angle-Doppler acoustic imaging of moving target

compressed sensing and reconstruction of underwater acoustic signals. Finally

the development trend of sparse representation theory in underwater acoustic signal processing is pointed out. Some necessary computer simulations are carried out to extract the multi-dimensional information features of underwater target in time

frequency and spatial domain are successfully extracted

and two kinds of typical communication signals are effectively compressed and accurately reconstructed.

关键词

Keywords

references

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