A novel method to detect and separate LFM signal based on artificial neural network

En Qing Chen; Ran Tao

A novel method to detect and separate LFM signal based on artificial neural network

En Qing Chen^*, Ran Tao

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

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

1 Citation (Scopus)

Abstract

A novel method using artificial neural network with back-propagation algorithm to detect and separate LFM signal is proposed. This method trains the network by LFM signal mixed with Gauss noise. Simulation result shows the trained BP neural network can eliminate noise effectively. In addition, if the learning sample is a multicomponent LFM signal, the trained network can separate the LFM signal component conveniently. Theoretical analysis and simulation results show that the proposed method has low computational complexity and good performance.

Original language	English
Title of host publication	Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Pages	32-35
Number of pages	4
Publication status	Published - 2005
Event	2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 - Beijing, China Duration: 13 Oct 2005 → 15 Oct 2005

Publication series

Name	Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Volume	1

Conference

Conference	2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Country/Territory	China
City	Beijing
Period	13/10/05 → 15/10/05

Cite this

@inproceedings{8a6ebbff2baa4504a31489a89f558b20,

title = "A novel method to detect and separate LFM signal based on artificial neural network",

abstract = "A novel method using artificial neural network with back-propagation algorithm to detect and separate LFM signal is proposed. This method trains the network by LFM signal mixed with Gauss noise. Simulation result shows the trained BP neural network can eliminate noise effectively. In addition, if the learning sample is a multicomponent LFM signal, the trained network can separate the LFM signal component conveniently. Theoretical analysis and simulation results show that the proposed method has low computational complexity and good performance.",

author = "Chen, {En Qing} and Ran Tao",

year = "2005",

language = "English",

isbn = "0780394224",

series = "Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05",

pages = "32--35",

booktitle = "Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05",

note = "2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 ; Conference date: 13-10-2005 Through 15-10-2005",

}

Chen, EQ & Tao, R 2005, A novel method to detect and separate LFM signal based on artificial neural network. in Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05., 1614562, Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05, vol. 1, pp. 32-35, 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05, Beijing, China, 13/10/05.

A novel method to detect and separate LFM signal based on artificial neural network. / Chen, En Qing; Tao, Ran.
Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05. 2005. p. 32-35 1614562 (Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05; Vol. 1).

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

TY - GEN

T1 - A novel method to detect and separate LFM signal based on artificial neural network

AU - Chen, En Qing

AU - Tao, Ran

PY - 2005

Y1 - 2005

N2 - A novel method using artificial neural network with back-propagation algorithm to detect and separate LFM signal is proposed. This method trains the network by LFM signal mixed with Gauss noise. Simulation result shows the trained BP neural network can eliminate noise effectively. In addition, if the learning sample is a multicomponent LFM signal, the trained network can separate the LFM signal component conveniently. Theoretical analysis and simulation results show that the proposed method has low computational complexity and good performance.

AB - A novel method using artificial neural network with back-propagation algorithm to detect and separate LFM signal is proposed. This method trains the network by LFM signal mixed with Gauss noise. Simulation result shows the trained BP neural network can eliminate noise effectively. In addition, if the learning sample is a multicomponent LFM signal, the trained network can separate the LFM signal component conveniently. Theoretical analysis and simulation results show that the proposed method has low computational complexity and good performance.

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M3 - Conference contribution

AN - SCOPUS:33847096453

SN - 0780394224

SN - 9780780394223

T3 - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

SP - 32

EP - 35

BT - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

T2 - 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

Y2 - 13 October 2005 through 15 October 2005

ER -

A novel method to detect and separate LFM signal based on artificial neural network

Abstract

Publication series

Conference

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