Optimum Bistatic Angle Extraction Using Compressed Time-Frequency Feature Vectors

Abstract

When the target of interest is determined, the transmitter and receiver positions of bistatic radar are of great importance at the aspect of radar target classification. The radar cross section (RCS) of a target varies with these positions, and the target classification performance is considerably influenced by RCS. In this study, the target classification performance using the bistatic scattering data of wire targets and scale-model targets is analyzed and compared. Time-frequency analysis and effective compression techniques are used for target feature extraction from the bistatic scattering data of each target, and a multilayered perceptron (MLP) neural network is used as a classifier. The optimum receiver position is found by comparing the calculated classification probabilities while changing the

Target identification using bistatic high‐resolution range profiles

“…Recently, several identification schemes using BS‐HRRPs have been proposed [5–8]. In [5], the SCs were extracted from the MS and BS radar signals using the one‐dimensional (1D) fast Fourier transform (FFT)‐based CLEAN algorithm.…”

“…In [7], time‐frequency analysis and effective compression techniques were used for target feature extraction from the complex BS‐HRRPs, and a multilayered perceptron neural network was used as a classifier. Furthermore, the optimum which can maximise the identification accuracy was found by changing the position of the BS radar receiver.…”

“…Furthermore, the optimum which can maximise the identification accuracy was found by changing the position of the BS radar receiver. But, the results in that method are unreliable, because the complex BS‐HRRPs used in [7] were generated from only limited 2D BS radar configurations; these are very unrealistic configurations. In addition, the concept of the finding optimum is inappropriate for real BS target identification, because real is varied depending on the 3D BS radar configurations in an actual situation (i.e.…”