Radar detection and parameter estimation of high-speed targets based on RFRT-SoWVD

Abstract

We propose a moving target detection (MTD) and parameter estimation algorithm based on the range frequency reversing transform (RFRT) and second-order Wigner–Ville distribution (SoWVD) to solve the problem of range migration and Doppler migration of air-moving targets during long-term coherent integration. The algorithm first performs the RFRT before multiplying it with the original data to remove range migration. Next, the fast Fourier transform is applied to realize the SoWVD algorithm to quickly estimate the target’s acceleration parameters. Finally, after compensating for the acceleration parameters, the scaled inverse Fourier transform (SCIFT) is employed with the target’s initial range and velocity. In the simulation experiment, compared with five representative algorithms—MTD, SCIFT, adjacent cross-correlation function and Lv’s distribution, three-dimensional scaled transform, and Radon–Fourier transform and keystone transform—the RFRT-SoWVD algorithm achieves a good balance between computational cost and detection probability. Moreover, we utilize real measured radar data of an unmanned aerial vehicle target to verify the RFRT-SoWVD algorithm. The results show that the proposed algorithm can achieve excellent detection and parameter estimation performance.