Correct and robust translational motion compensation is necessary but challenging for inverse synthetic aperture radar (ISAR) imaging and target recognition. On the one hand, parametric translational motion compensation methods only effective for polynomial translational models, on the other hand, the noise robustness of range alignment-autofocus methods is not satisfactory. Therefore, this work proposes a joint range alignment and autofocus method based on Combined Broyden-Fletcher-Goldfarb-Shanno algorithm and whale optimization algorithm (BFGS-WOA). The method aims to achieve accurate and robust compensation of profile shift and the phase error simultaneously without relying on a specific translational model. Specifically, we use Laplacian entropy and squared envelope entropy to construct a dynamic objective function. It can improve the robustness of translational error estimation. The translational error of each pulse can be estimated by minimizing the objective function. We adopt BFGS to solve the optimization to ensure the global convergence. WOA is introduced to determine the optimal step size of the iteration. Finally, the estimated translational error is used to perform range alignment and autofocus simultaneously. Experimental results of measured datasets demonstrate that the proposed method outperforms existing methods and has strong robustness for low signal to noise ratio (SNR) and sparse aperture.

In inverse synthetic-aperture radar (ISAR) imaging, it is essential to deal with the Doppler ambiguity of group targets with complex maneuvers in order to avoid the bias of target position towards the actual value. Simultaneously, migration through resolution cell (MTRC) under the Doppler ambiguity is unable to be compensated for as a preprocessing. Traditional ISAR imaging methods for maneuvering targets, however, are undesirable to handle the severe deformation and defocusing in the imaging results induced by the Doppler ambiguity and MTRC. In this paper, we propose a novel and effective ISAR imaging method to improve the imaging quality by removing the Doppler ambiguity and compensating for the MTRC. Specifically, we first model the echo as a multi-component cubic phase signal (m-CPS) and design a high-order instantaneous autocorrelation function–generalized scaled Fourier transform (HIAF–GSCFT) to process the echo. This is to estimate the rotational parameters without MTRC compensation. Then, a maximum weighted contrast algorithm is used to remove the Doppler ambiguity, followed by reconstructing the echo. Compared with the existing method, the proposed method can accurately estimate the rotational parameters under the existing MTRCs and achieves a high-quality ISAR image for group targets, with complex maneuvers without Doppler ambiguity. Experiments of simulated and measured datasets validate its effectiveness and robustness for single target and group targets.

Translational motion compensation for maneuvering targets is a challenging step in inverse synthetic aperture radar (ISAR) imaging. On one hand, nonuniform translational motion and rotational motion of maneuvering targets result in nonlinear bending of the range profile and create high-order slow time phases. On the other hand, radar cross section (RCS) fluctuation of targets may cause a sparse aperture and, therefore, sharply weakens the correlation between the pulses. Under this condition, traditional methods of translational motion compensation are sometimes difficult to get ideal results. Therefore, we propose an effective and novel method to achieve translational motion compensation for maneuvering targets with sparse aperture. In this method, the translational and rotational motions are modeled as cubic polynomial and quadratic polynomial, respectively. Based on this model, we use the echo to construct a 1-D optimization to estimate all translational parameters simultaneously and use the root-mean-square prop-momentum gradient descent (RMSprop-MGD) algorithm to solve the optimization to maintain the accuracy of parameter estimation. Finally, we use those translational parameters to compensate for the translational motion. Since this method converts the multiparameter optimization into a 1-D optimization, we named it dimension compressed optimization (DCO). Experimental results of the measured dataset prove that the proposed method is effective for full-aperture echoes, random sparse echoes, and block sparse echoes. Moreover, this method is robust under a low pulse sampling rate and low signal-to-noise ratio (SNR) environment.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.

In ISAR imaging, echo signals of complex manoeuvring targets have to be modelled as multi‐component cubic phase signals (m‐CPS). In this condition, the Doppler diffusion will seriously affect the quality of ISAR imaging. Herein, a complex manoeuvring target imaging method based on local polynomial transform‐fast chirp Fourier transform (LPT‐FCFT) is proposed. In this method, the instantaneous frequency and the quadratic chirp rate of the echo signal are estimated by local polynomial transformation, then the chirp rate and the amplitude are estimated by fast chirp Fourier transform, and finally the high‐resolution ISAR image of the complex manoeuvring target is obtained by CLEAN technology. Compared with the traditional method, the proposed method has the advantages of less computational complexity, clearer imaging results and better anti‐noise performance. Simulation results indicate the effectiveness and superiority of the proposed method.

Inverse synthetic aperture radar (ISAR) images have become one of the most important pieces of information for airborne and maritime target identification. In general, ISAR images with higher resolution and lower background noise provide more precise target information, thus improving target identification accuracy. However, upgrading the resolution of the ISAR system is costly. Super-resolution algorithms that can utilize low-resolution echoes to obtain high-resolution imaging results have become an important means of improving ISAR imaging resolution. The traditional ISAR super-resolution imaging technique suffers from high side lobes and wide main lobes. In addition, de-noising algorithms based on filtering operators tend to lead to image blurring. This work proposes a deep network for super-resolution and de-noising of ISAR images called SR-DNnet. Specifically, we view super-resolution and de-noising as a series of up-sampling, two-dimensional filtering, and threshold shrinkage. These operations are exactly what deep networks are good at. SR-DNnet has 15 layers, enabling 4x super-resolution and de-noising of ISAR images. The parameter scale of SR-DNnet is much smaller than most deep networks, which makes it efficient to train. The SR-DNnet we built features complex-value inputs, residual learning, multipath learning, and progressive up-sampling. A series of simulated and measured dataset experiments prove that the SR-DNnet is efficient and well-performed on super-resolution and de-noising.