HRRPs Reconstruction of Multiple High-Speed Targets With Large Time-Bandwidth Product LFM Waveform Based on Efficient SBL

High-resolution range profiles (HRRPs)-based ballistic target recognition and intercept kill assessment are critical missions in ballistic missile defense. However, the high-speed motion brings nonnegligible in-pulse Doppler effect, which makes the HRRP distorted, especially in transmitting large time-bandwidth product waveform. Meanwhile, there are multiple high-speed targets before and after the moment of interception in different flight states. In this article, the method for HRRPs reconstruction of multiple high-speed targets is proposed based on minimum entropy fast Fourier transform (FFT)-CLEAN and efficient sparse Bayesian learning. First, multiple high-speed targets are not suitable for simultaneous processing in the HRRPs reconstruction process. A minimum entropy FFT-CLEAN algorithm is proposed, which can detect and separate the echo of each target, and obtain the coarse velocity estimate. Second, the HRRPs reconstruction and accurate velocity estimation are unified into the sparse Bayesian framework. To improve the reconstruction efficiency, Gohberg–Semencul factorization and preconditioned conjugate gradient method are used in the sparse Bayesian learning inference process. Finally, simulation and measurement results demonstrate that the proposed algorithm achieves better HRRP reconstruction performance and higher computational efficiency.