DocumentCode
2510467
Title
Radar emitter classification using self-organising Neural Network models
Author
Anjaneyulu, L. ; Murthy, N.S. ; Sarma, N. V S N
Author_Institution
Dept. of ECE, Nat. Inst. of Technol., Warangal
fYear
2008
fDate
21-24 Nov. 2008
Firstpage
431
Lastpage
433
Abstract
This paper presents a radar emitter identification and classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitterpsilas parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately identify and classify the emitter types.
Keywords
direction-of-arrival estimation; fuzzy neural nets; radar computing; radar detection; self-organising feature maps; ARTMAP neural networks; Fuzzy ART; direction of arrival; radar emitter classification; self-organising neural network models; Artificial neural networks; Biological system modeling; Fuzzy neural networks; Neural networks; Pulse measurements; Radar detection; Radio frequency; Radiofrequency identification; Signal processing; Subspace constraints; ARTMAP; Artificial Neural Networks; EID; Fuzzy ART; Radar Emitter;
fLanguage
English
Publisher
ieee
Conference_Titel
Recent Advances in Microwave Theory and Applications, 2008. MICROWAVE 2008. International Conference on
Conference_Location
Jaipur
Print_ISBN
978-1-4244-2690-4
Electronic_ISBN
978-1-4244-2691-1
Type
conf
DOI
10.1109/AMTA.2008.4763033
Filename
4763033
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