Sea Clutter Suppression and Micromotion Target Detection in Sparse Domain

CHEN Xiao-long; GUAN Jian; DONG Yun-long; ZHAO Zhi-jian

doi:10.3969/j.issn.0372-2112.2016.04.015

您当前的位置：

首页 >

文章列表页 >

Sea Clutter Suppression and Micromotion Target Detection in Sparse Domain

更新时间：2025-07-16

- Sea Clutter Suppression and Micromotion Target Detection in Sparse Domain
- Acta Electronica Sinica Vol. 44, Issue 4, Pages: 860-867(2016)
- 作者机构：
  
  1. 海军航空工程学院电子信息工程系,山东,烟台,264001
  2. 海军航空工程学院信息融合研究所,山东,烟台,264001
  3. 海军航空工程学院接改装训练大队,山东,烟台,264001
  4. 海军航空工程学院电子信息工程系,山东,烟台,264001
  5. 海军航空工程学院信息融合研究所,山东,烟台,264001
  6. 海军航空工程学院接改装训练大队,山东,烟台,264001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.04.015
  CLC： TN911.23
- Published Online：25 April 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
CHEN Xiao-long, GUAN Jian, DONG Yun-long, et al. Sea Clutter Suppression and Micromotion Target Detection in Sparse Domain[J]. Acta Electronica Sinica, 2016, 44(4): 860-867.
DOI：

CHEN Xiao-long, GUAN Jian, DONG Yun-long, et al. Sea Clutter Suppression and Micromotion Target Detection in Sparse Domain[J]. Acta Electronica Sinica, 2016, 44(4): 860-867. DOI： 10.3969/j.issn.0372-2112.2016.04.015.

摘要

针对海上微动目标回波信号具有稀疏性的特点

该文研究了稀疏域微动特征提取和检测方法

提出一种基于形态成分分析(MCA)的海杂波抑制与微动目标检测方法.该方法充分利用海杂波和微多普勒信号组成成分的形态差异性

对不同源信号采用不同的字典进行稀疏表示

区分海杂波与微动目标.此外

提出的稀疏域海杂波抑制方法

能够在抑制海杂波的同时积累更多的信号能量

改善信杂比.仿真和实测数据验证了算法的正确性.

Abstract

Using the sparse property of the signal from a marine micromotion target

the extraction and detection of micromotion signatures in sparse domain are studied.An algorithm for sea clutter suppression and micromotion target detection is proposed based on the morphological component analysis (MCA).The algorithm takes full advantage of the morphological differences between sea clutter and micro-Doppler signal

and can separate them via sparse representation of different source signals using different dictionaries.Moreover

the proposed sea clutter suppression method in sparse domain can achieve both target's energy accumulation and sea clutter suppression with improved signal-to-clutter ratio (SCR).Simulated and real data all verify the effectiveness of the proposed method.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Matrix CFAR Detector Based on Matrix Spectral Norm

Clutter Modeling and Radar Target Detection Method Based on Deep Unsupervised Variational Networks

A Spatiotemporal Fusion Algorithm of Remote Sensing Images Based on Cross-Scale Similarity Prior

Information Geometry Detector Based on Joint Optimization of Feature and Metric

Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model

Related Author

ZHAO Wen-jing

JIN Ming-lu

LIU Wen-long

LIU Yao-qiang

CHEN Wen-chao

SHI Li-xing

TIAN Long

WANG Peng-hui

Related Institution

School of Information and Communication Engineering, Dalian University of Technology

National Key Laboratory of Radar Signal Processing, Xidian University

School of Electronic Engineering, Xidian University

School of Computer Science and Technology, Xidian University

School of Computer and Information, Hefei University of Technology

⁰