Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6946809
