OSGOS-CFAR ALGORITHM BASED ON CLASSIFICATION RECOGNITION

Chengcheng Yu; Yanmei Zhang; Jiawe Luo; Meifang Xiao

doi:10.1049/icp.2021.0376

OSGOS-CFAR ALGORITHM BASED ON CLASSIFICATION RECOGNITION

Chengcheng Yu, Yanmei Zhang^*, Jiawe Luo, Meifang Xiao

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, an OSGOS-CFAR algorithm based on classification recognition is proposed. With the increasingly complex clutter environment, there are still many problems and challenges in CFAR technology. In this algorithm, the classification and identification of clutter based on convolutional neural network (CNN) and parameter estimation are carried out to obtain the threshold factor suitable for specific clutter. Then, the constant false alarm rate (CFAR) processing of clutter is carried out combined with OSGOS-CFAR algorithm. As a result, the algorithm has good detection rate on the premise of ensuring false alarm rate.

Original language	English
Title of host publication	IET Conference Proceedings
Publisher	Institution of Engineering and Technology
Pages	1157-1160
Number of pages	4
Volume	2020
Edition	3
ISBN (Electronic)	9781839534195
DOIs	https://doi.org/10.1049/icp.2021.0376
Publication status	Published - 2020
Event	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 - Virtual, Online Duration: 18 Sept 2020 → 21 Sept 2020

Conference

Conference	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020
City	Virtual, Online
Period	18/09/20 → 21/09/20

Keywords

CFAR
CLASSIFICATION AND IDENTIFICATION
CONVOLUTIONAL NEURAL NETWORK

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10.1049/icp.2021.0376

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@inproceedings{5f83a6d8e55c4c839b81bb8e207608fa,

title = "OSGOS-CFAR ALGORITHM BASED ON CLASSIFICATION RECOGNITION",

abstract = "In this paper, an OSGOS-CFAR algorithm based on classification recognition is proposed. With the increasingly complex clutter environment, there are still many problems and challenges in CFAR technology. In this algorithm, the classification and identification of clutter based on convolutional neural network (CNN) and parameter estimation are carried out to obtain the threshold factor suitable for specific clutter. Then, the constant false alarm rate (CFAR) processing of clutter is carried out combined with OSGOS-CFAR algorithm. As a result, the algorithm has good detection rate on the premise of ensuring false alarm rate.",

keywords = "CFAR, CLASSIFICATION AND IDENTIFICATION, CONVOLUTIONAL NEURAL NETWORK",

author = "Chengcheng Yu and Yanmei Zhang and Jiawe Luo and Meifang Xiao",

note = "Publisher Copyright: {\textcopyright} 2020 The Institution of Engineering and Technology.; 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 ; Conference date: 18-09-2020 Through 21-09-2020",

year = "2020",

doi = "10.1049/icp.2021.0376",

language = "English",

volume = "2020",

pages = "1157--1160",

booktitle = "IET Conference Proceedings",

publisher = "Institution of Engineering and Technology",

address = "United Kingdom",

edition = "3",

}

TY - GEN

T1 - OSGOS-CFAR ALGORITHM BASED ON CLASSIFICATION RECOGNITION

AU - Yu, Chengcheng

AU - Zhang, Yanmei

AU - Luo, Jiawe

AU - Xiao, Meifang

PY - 2020

Y1 - 2020

N2 - In this paper, an OSGOS-CFAR algorithm based on classification recognition is proposed. With the increasingly complex clutter environment, there are still many problems and challenges in CFAR technology. In this algorithm, the classification and identification of clutter based on convolutional neural network (CNN) and parameter estimation are carried out to obtain the threshold factor suitable for specific clutter. Then, the constant false alarm rate (CFAR) processing of clutter is carried out combined with OSGOS-CFAR algorithm. As a result, the algorithm has good detection rate on the premise of ensuring false alarm rate.

AB - In this paper, an OSGOS-CFAR algorithm based on classification recognition is proposed. With the increasingly complex clutter environment, there are still many problems and challenges in CFAR technology. In this algorithm, the classification and identification of clutter based on convolutional neural network (CNN) and parameter estimation are carried out to obtain the threshold factor suitable for specific clutter. Then, the constant false alarm rate (CFAR) processing of clutter is carried out combined with OSGOS-CFAR algorithm. As a result, the algorithm has good detection rate on the premise of ensuring false alarm rate.

KW - CFAR

KW - CLASSIFICATION AND IDENTIFICATION

KW - CONVOLUTIONAL NEURAL NETWORK

UR - http://www.scopus.com/inward/record.url?scp=85174650949&partnerID=8YFLogxK

U2 - 10.1049/icp.2021.0376

DO - 10.1049/icp.2021.0376

M3 - Conference contribution

AN - SCOPUS:85174650949

VL - 2020

SP - 1157

EP - 1160

BT - IET Conference Proceedings

PB - Institution of Engineering and Technology

T2 - 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020

Y2 - 18 September 2020 through 21 September 2020

ER -

OSGOS-CFAR ALGORITHM BASED ON CLASSIFICATION RECOGNITION

Abstract

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Keywords

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