A Novel ISAR Imaging Approach for Moving Targets Utilizing Joint Particle Swarm Optimization and Time Polynomial Rescaling-Nonuniform FFT

For maneuvering targets with weak surface reflections, the conventional ISAR imaging algorithms based on the motion parameter estimation for each scatterer may have a poor performance under low signal-to-noise ratio (SNR) scenario. To tackle this problem, a novel ISAR imaging algorithm is proposed in this letter. The effects of translational motion, the high-order migration through resolution cells (MTRC), and the nonstationary phase distribution are simultaneously eliminated by constructing the compensation function and utilizing the time polynomial rescaling-nonuniform fast Fourier transform (TPRS-NUFFT). Then, the particle swarm optimization (PSO) algorithm is applied to accomplish the motion parameter estimation. Finally, a high-resolution ISAR image can be obtained after motion compensation. Both simulation and real-measured data processing results demonstrate that the proposed algorithm can obtain focused ISAR image and improve the SNR by 29.8 dB.