High Maneuvering Target Long-Time Coherent Integration and Motion Parameters Estimation Based on Bayesian Compressive Sensing

This article intends to address the long-time coherent integration of high maneuvering targets in low signal-to-noise environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the underestimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on noncoherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution, the proposed BCS algorithm is free from the interference of cross-terms and maintains the superresolution ability, which provides better performance in multitargets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, superresolution ability, and computational efficiency.