Computational efficient segmented integration method for high-speed maneuvering target detection

Abstract

Long-time integration can improve the radar detection performance of the moving targets. However, the high speed maneuvering targets (HSMT) would cause range migration (RM) and Doppler Spread (DS) during the long integration process, which would lead to the failure of traditional moving target detection (MTD) method. The existed Generalized Radon-Fourier transform (GRFT) method can achieve a good integration and detection performance using multi-dimensional searching among the target parameter space, but the computational burden is too large. We propose a computational efficient segmented integration method for the HSMT detection, which could reduce the computational cost with slight integration performance loss. First, the echo signal is segmented based on the criterion that the RM and DS caused by the acceleration in each sub-segment can be ignored. After that, the proposed method obtains the integration of each sub-segment through Frequency bin Randon Fourier transform (FBRFT), while FBRFT can be quickly realized through Chirp-z transform (CZT). Subsequently, by analyzing the amplitude characteristics and phase characteristics of the FBRFT output, a matched accumulation method is presented to coherently integrate the energy among different sub-segments to further improve the signal-to-noise ratio (SNR). Finally, the performance of the proposed algorithm is analyzed through simulation experiments.