Bistatic radar target identification using FFT-based CLEAN

Author

In-Sik Choi ; Seung-Jae Lee

Author_Institution

Dept. of Electron. Eng., Chungnam Nat. Univ., Daejeon, South Korea

fYear

2014

fDate

13-18 July 2014

Firstpage

1825

Lastpage

1828

Abstract

In this paper, we compared the performance of bistatic radar target identification using the computed bistatic RCS of the full-scale targets. The FFT (fast Fourier transform)-based CLEAN is used as the feature vector extraction method and multi-layered perceptron (MLP) neural network is used as a classifier. Simulation results show that the optimally positioned bistatic radar has better target identification performance, demonstrating the importance of the transmitter and receiver positions in bistatic radar.

Keywords

electrical engineering computing; fast Fourier transforms; feature extraction; multilayer perceptrons; pattern classification; radar receivers; radar transmitters; vectors; FFT-based CLEAN; MLP neural network; bistatic radar target identification; classifier; computed bistatic RCS; fast Fourier transform; feature vector extraction method; multilayered perceptron neural network; radar receiver; radar transmitter; Bistatic radar; Feature extraction; Radar cross-sections; Receivers; Scattering; Transmitters; bistatic radar; radar target identification; scattering centers;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International

Conference_Location

Quebec City, QC

Type

conf

DOI

10.1109/IGARSS.2014.6946809

Filename

6946809