In the high-resolution inverse synthetic aperture radar (ISAR) imaging of low-earth-orbit space targets, the received signal from one range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. The chirp rates and the quadratic chirp rates of the multicomponent CPS need to be estimated with parameter estimation algorithms to obtain the focused ISAR image. In this article, a new parametric range-instantaneous-Doppler ISAR imaging method is proposed, which introduces a new algorithm for parameter estimation of multicomponent CPSs. The cross terms of the generalized cubic phase function (GCPF) are analyzed. By eliminating the cross terms in the ambiguity function domain, the coherent integrated smoothed GCPF (CISGCPF) is proposed to estimate the quadratic chirp rates. Numerical examples are provided to verify the accuracy of the parameter estimation and the effectiveness of the cross-term suppression for CISGCPF in processing multicomponent signals. Simulated and real data imaging results demonstrate the performance of the CISGCPF-based ISAR imaging method. Comparisons with the existing algorithms show that the proposed method is effective in reconstructing focused images with less fake scatterers.

For inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, and hence, the traditional range-Doppler (RD) algorithm is not appropriate for obtaining focusing ISAR images. In such a scenario, a novel ISAR technique, called the range instantaneous Doppler derivative (RIDD) algorithm, has been recently proposed, and two different kinds of well-focused ISAR images, named range-Doppler centroid (RDC) frequency image and range-Doppler frequency rate (RDR) image, can be obtained. In order to improve the accuracy of the target classification and recognition, the scaling approach for the RIDD algorithm must be taken into consideration. In this article, the point spread function (PSF) representations and cross-range scaling factors of the RDC image and the RDR image obtained by the RIDD algorithm are demonstrated analytically. On the basis of the PSFs and cross-range scaling factors, a novel imaging and scaling approach for the RIDD algorithm in the presence of a low SNR environment based on smoothed integrated high-resolution time–frequency-rate representation (SIHR) combined with coherent integrated smoothed generalized cubic phase function (CISCF) is proposed. As the proposed algorithm has high estimation accuracy and good antinoise capability, precise parameter estimation of the received signal can be achieved. Hence, high-quality scaled RDC and RDR images of the target can be reconstructed by the proposed approach. Experimental results of simulated data and real measured data verify the correctness of the PSF representations and demonstrate the effectiveness of the imaging and scaling approach proposed in this article.

For air maneuvering targets, the higher-order components in the translational motion can induce severe range shifts and phase errors. They can deteriorate the performance of translational motion compensation (TMC) in the inverse synthetic aperture radar (ISAR) imaging process. Additionally, for weak targets, low signal-to-noise ratio (SNR) can also severely affect the accuracy of TMC. In this letter, a novel TMC method for ISAR imaging of air maneuvering weak targets based on CV-GRUNet is proposed. First, leveraging the noise robustness and capability to handle time series of the Gated Recurrent Unit (GRU), it is extended to the complex domain to construct CV-GRU. CV-GRU can effectively extract the features of range shifts and higher-order phase errors. Subsequently, CV-GRUNet is constructed to obtain the compensation function. Finally, TMC can be achieved using the compensation function, resulting in well-focused ISAR images. Compared with existing methods, the proposed method can achieve accurate TMC for ISAR imaging of maneuvering weak targets. The compensation accuracy of the simulated target has reached 1/20 of the wavelength. Simulated and real data results verify the effectiveness and superiority of the proposed method.

Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GANs) achieve great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real and imaginary parts of the data. To this end, an end-to-end refocusing network, named complex-valued pix2pixHD (CVPHD), is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as an input. A CV instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. The code and dataset have been available online ( https://github.com/yhx-hit/CVPHD ).

For inverse synthetic aperture radar (ISAR) imaging of the maneuvering target, when the time-varying rotational acceleration arises, the image will become blurred because of high-order phase terms. In this letter, the received signal in a range bin is modeled as the multicomponent high-order polynomial phase signal (PPS). To eliminate high-order phase terms, a novel ISAR imaging algorithm based on the cubic phase function and changing sampling rate (CPF-CSR) is proposed in this letter. First, we regard the high-order PPS as a piecewise cubic phase signal (CPS), and thus the cubic phase function (CPF) algorithm can estimate the instantaneous frequency rate (IFR). Next, to suppress cross-terms for the multicomponent PPS, an improved algorithm based on the CPF is proposed, which searches the range bin composed of multiple strong point scatterers and estimates the IFR of the dominant one. Finally, the changing sampling rate (CSR) algorithm is proposed to eliminate high-order phase terms by adjusting the instantaneous frequency of the signal, and no additional estimation for the IFR of other components is required. After the cross-range compression, a well-focused ISAR image can be obtained. The experimental results of the simulated data and real data are given to validate the effectiveness of the CPF-CSR algorithm.