Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, an efficient range-Doppler domain ISAR imaging method for rapidly spinning targets is proposed in this article. First, echo signal is transformed into range-Doppler domain and its precise analytical expression is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius. By doing so, the energy is concentrated in the same range cell. After that, the high-order phase terms of the signal are compensated and the CLEAN technique is also applied to reduce the sidelobes of a strong scatterer. Finally, 3-D ISAR image of the spinning target is reconstructed by projecting the spatial parameters to 3-D cylindrical coordinates. Furthermore, in this article, the output signal-to-noise ratio (SNR) gain, anti-noise performance, the mismatched phase error, and the computational complexity analyses are also provided. Compared with existing approaches, the proposed method has advantages in the computational complexity and low SNR environment thanks to the only 1-D search and the coherent integration gain obtained. Both the theoretical derivations and the simulated results demonstrate the effectiveness of the proposed method.

Owing to the highly maneuvering character involved in targets, the nonuniform 3-D rotation motions make the assumption that the image projection plane (IPP) is constant during coherent processing interval (CPI) invalid. In this work, an efficient approach is proposed in ISAR imaging for highly maneuvering targets with nonstationary IPP. First, to reasonably describe the mobility of a highly maneuvering motion target, the geometry and signal model with nonstationary IPP are established, where the high-order phase model is deduced to describe the 2-D spatial-variant phase errors. Second, based on the developed signal model, considering the cost function obtained via conventional image entropy with local extremum, the subarray averaging operation in conjunction with entropy is utilized to accelerate the global optimal convergence. Finally, the accurate 2-D spatial-variant phase errors compensation terms are generated to produce the well-focused ISAR images. Compared with existing methods, the main advantages of this work are: 1) the geometry and signal model of the target with nonstationary IPP are established; 2) the subarray averaging operation in conjunction with image entropy is utilized to accelerate the global optimal convergence; and 3) the high-order signal model is derived to present the 2-D spatial-variant phase errors. Several numerical experiments using simulated data and electromagnetic data are conducted to demonstrate the validity of the proposed algorithm and signal model.

The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.

Inverse synthetic aperture radar (ISAR) imaging for non-cooperatives targets with complex translational motion (TM) and 3D rotational motion (RM) face the problem of spatial-variant (SV) and high-order phase modulation. Existing ISAR technique cannot compensate for the phase modulation completely, especially under low signal-to-noise-ratio (SNR). In this work, an efficient ISAR imaging approach is proposed for non-cooperative targets under low SNR. First, the signal model for non-cooperative targets with TM and 3D RM are established, where the high-order 2-D spatial-variant phase error are deduced by utilizing a nonstationary image projection plane (IPP) model. Second, to mitigate the influence of noise, an adaptive de-noising filter is generated by exploring the similarity between the profiles of echoes. In addition, inspired by the characteristic that all scatterers share the same TM, the compensating factors are extracted from the prominent scatterers, which can absolutely avoid the accumulation of residual TM errors. Meanwhile, the signal coherence is fully utilized to compensate for the SV phase errors caused by the RM. Finally, both simulated and electromagnetic data experiments validate the effectiveness and robustness of the proposed method.

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to azimuth time-varying Doppler frequency modulation, especially under moving platform condition. In this case, the common assumption that the image projection plane (IPP) of the radar line-of-sight (LOS) direction is constant during coherent processing interval (CPI) is invalid. To address this issue, a novel ISAR imaging approach for maneuvering targets is proposed by exploiting nonstationary IPP in this article. First, considering time-varying LOS direction, the new geometric and signal models are developed, where 2-D spatial-variant phase error is mainly deduced. After that, a parametric image entropy minimum optimization combined with efficient particle swarm optimization (PSO) is used to obtain optimal motion parameters. In doing so, 2-D spatial-variant phase error terms are compensated accurately to produce well-focused ISAR image. Finally, the effectiveness and superiority of the proposed algorithm are verified by the simulation results and electromagnetic scattering data.