An improved cross-correlation approach to parameter estimation based on fractional Fourier transform for ISAR motion compensation

Abstract

Motion compensation (MOCOMP) is a key procedure in inverse synthetic aperture radar (ISAR) imaging because the accuracy of estimated parameter has a strong influence on the imaging quality. Generally, the backscattered signal of a moving target is sampled in fast time dimension, which can be approximated as the combination of multiple Chirp signals with a proper Chirp rate. Compared with the Fourier transform, the fractional Fourier transform (FrFT) performs better compression property due to its unique energy focus ability to Chirp signals. An improved Cross-correlation method based on FrFT for parameter estimation is presented in this paper. It employs the correlation between range profiles compressed by FrFT to enhance the quality of parameter estimation for ISAR applications. The method takes good balance between accuracy and complexity, and is robust to noise. Simulation results show that the proposed method outperforms the conventional Cross-correlation Method in terms of ISAR translational MOCOMP.