Centrosymmetric-Transform-Based Coherent Integration Approach for Maneuvering Targets With Jerk

In this article, a long-time coherent integration approach is proposed for a maneuvering target with jerk. In this approach, the third-order keystone transform is first employed to correct the cubic range migration induced by jerk. Then, the centrosymmetric transform, scaled inverse Fourier transform, and nonuniform fast Fourier transform (NUFFT) are executed sequentially to accomplish the velocity and acceleration estimation. Thereafter, based on the estimated parameters, the phase compensation function is constructed to remove the influences of velocity and acceleration terms. Based on this, the inverse fast Fourier transform (IFFT) and the NUFFT are performed to accomplish coherent integration in the range–jerk domain. Moreover, a comprehensive discrimination procedure is proposed to identify potential spurious peaks formed by cross components in the case of multitarget scene. Compared with the keystone transform and matched filtering, the proposed method can be efficiently implemented thanks to the avoidance of multidimensional grid search. In contrast with the conventional correlation-based methods, the proposed approach may achieve better antinoise performance attributed to involving only one nonlinear operation. Therefore, the presented approach may strike a good equilibrium between antinoise capability and computational load. Simulation and real measured data processing results prove the effectiveness of the presented method.