Aircraft identification using a multi-stage fuzzy neural network

Author

Xiaojian, Xu ; Constantinides, A.G.

Author_Institution

Electromagnetic Scattering & Radiat. Lab., China Nat. Space Adm., Beijing, China

fYear

1996

fDate

8-10 Oct 1996

Firstpage

151

Lastpage

155

Abstract

The effectiveness of time-frequency representation of ultra-wideband radar target signatures, in conjunction with a multi-stage fuzzy neural network (MSFFN), is investigated in the context of aircraft identification. Experimental results on the backscattered data of six aircraft models show that a good level of identification rate is possible at signal-to-noise ratios (SNR) as low as 5 dB

Keywords

aircraft; backscatter; fuzzy neural nets; radar cross-sections; radar signal processing; radar target recognition; signal representation; signal resolution; time-frequency analysis; aircraft identification; aircraft models; backscattered data; experimental results; high resolution radar; identification rate; multistage fuzzy neural network; signal-to-noise ratios; time-frequency representation; ultrawideband radar target signatures; Aerospace electronics; Airborne radar; Aircraft; Electromagnetic scattering; Fuzzy neural networks; Radar scattering; Radar signal processing; Space technology; Time frequency analysis; Ultra wideband technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar, 1996. Proceedings., CIE International Conference of

Conference_Location

Beijing

Print_ISBN

0-7803-2914-7

Type

conf

DOI

10.1109/ICR.1996.573794

Filename

573794