Technology of Network & Communication
|
1178-1180,1185

Application of improved DNN algorithm in radar signal sorting

Chen Chunli
Jin Weidong
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Abstract

The advantage of deep neural network to automatically learn the deep characteristics of data was studied. This paper proposed a signal sorting method based on multilayer deep belief networks, in order to solve the problems of time consuming in traditional radar signal selection, feature redundancy and so on. Based on the improved algorithm of depth of stacked multilayer model, it overcame the problem of insufficient to the single model of learning ability, and deeply studied the essential features of the different signal, and fused the posterior probability of the model to make a classification decision, so as to further improve the signal recognition rate. It used this method to sort 7 different types of radar emitter signal sorting. Compared with other performance signal sorting method, the experimental results show that this method obtains better classification results, and exhibits strong learning ability to nature features, thus it verifies the effectiveness and superiority of this algorithm.

Foundation Support

国家自然科学基金资助项目(61461051)
国家科技支撑计划资助项目(2015BAG14B01-05)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.09.1005
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Technology of Network & Communication
Pages: 1178-1180,1185
Serial Number: 1001-3695(2019)04-048-1178-03

Publish History

[2019-04-05] Printed Article

Cite This Article

陈春利, 金炜东. 一种改进的DNN算法在雷达信号分选中的应用 [J]. 计算机应用研究, 2019, 36 (4): 1178-1180,1185. (Chen Chunli, Jin Weidong. Application of improved DNN algorithm in radar signal sorting [J]. Application Research of Computers, 2019, 36 (4): 1178-1180,1185. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)