Determination of optimum bistatic angle for radar target identification

Abstract

The transmitter and receiver positions of a bistatic radar are highly influential on its performance in radar target identification since the radar cross-section of a target varies with these positions. In this study, radar target identification performance using calculated bistatic scattering data for three full-scale models and measured data for four-scale-model targets is analyzed and compared. FFT-based CLEAN is used for shift-invariant feature extraction from the bistatic scattering data of each target, and a multilayered perceptron neural network is used as a classifier. The optimum receiver position is found by comparing the calculated identification probabilities while changing the position of the bistatic radar receiver. The identification results using calculated data and measured data show that an optimally positioned bistatic radar yields better identification results, demonstrating the importance of the positions of the transmitter and receiver for bistatic radar.