Compared with ground-based radar, spaceborne inverse synthetic aperture radar (ISAR) has advantages in space situational awareness. Since the motion of the target relative to the radar is complex and nonuniform, high-order spatial variant phase (SVP) can be induced, resulting in a defocused image. Due to the unknown spatial distribution of scatterers, the correction of high-order SVP is a hard task. In this letter, a novel SBISAR imaging method for space targets based on high-order SVP correction (HOSVPC) is proposed. First, the geometric model for SBISAR imaging of space targets is established, and the slant range model is derived. The specific form of the high-order SVP is derived, which is modeled as the sum of the range term and the velocity term. Second, to correct the high-order SVP, an orbit parameter estimation method and the HOSVPC algorithm are proposed. The range term is compensated first using the orbit parameters, the velocity term is then corrected using non-uniform resampling, and the first-order term of the range term is finally compensated back. Finally, a well-focused ISAR image is obtained through cross-range compression. Scatterer simulation and electromagnetic simulation results demonstrate the effectiveness of the proposed algorithm.

The radar echo of a target can be modeled as a 2-D signal whose variables are the intra-pulse sampling time (fast-time) and the inter-pulse sampling time (slow-time). The fast-time instantaneous frequency (FIF) and slow-time instantaneous frequency (SIF) of the signal are modulated by the target's slant range. When the target undergoes complex motion, the radar echo becomes a 2-D signal with fast-varying instantaneous frequencies (IFs). IF analysis for such a signal is challenging. To solve this issue, an extending complex-lag time-frequency distribution (ECTD) is introduced. ECTD is a 3-D distribution for 2-D signals based on traditional complex-lag time-frequency distribution (CTD), and it inherits the good performance in handling fast-varying IFs. By introducing complex-lags in both the fast-time and slow-time dimensions, the ECTD can accurately estimate the SIF and FIF of a 2-D signal with fast-varying IFs. Finally, a reduced interference realization of the ECTD achieved by introducing the frequency domain filter is given. Numerical examples validate the effectiveness of the ECTD. The source code is provided athttps://github.com/XinhangZhu/ECTD.

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.

Due to the large imaging scene of geosynchronous synthetic aperture radar (GEO SAR), multiple ship targets are more likely to appear in the same imaging scene. When the targets overlap in the range dimension, their echo signals after range compression will also overlap and interfere with each other. Besides, the stop-and-go assumption is no longer valid for GEO SAR, and a long coherent processing interval (CPI) will result in significant range migration. In such a scenario, essential imaging processes, including the signal division algorithm for eliminating the overlap interference and the focusing process for echo signal parameter estimation, are difficult to implement. To address these issues, a signal division and focusing algorithm based on multiscale fast-time delay fractional Fourier transform (MFDFrFT) is proposed in this article. To describe the relationship between slant range and propagation distance under the non-stop-and-go assumption, a conversion ratio is introduced into the slant range model. Then, the echo signal model can be characterized as a multicomponent 2-D quadratic phase signal with fast-time delay (2-D FD-QPS). In order to directly analyze the parameters of this signal, a linear reversible transformation named MFDFrFT is proposed. Then, the echo signal of each individual target can be divided, and the estimated parameters can be achieved. With the estimated parameters, the focusing process can be implemented to finally obtain the well-focused images of the targets. Simulation and equivalent experiments are provided to validate the effectiveness of the proposed algorithm.

Due to scatterer occlusion and missing during the observation time, the acquisition of the complete scatterer trajectory matrix is a hard task, which is the most important process in 3-D inverse synthetic aperture radar (ISAR) imaging of space targets based on 2-D ISAR image sequence. To cope with this problem, a novel fast scatterer trajectory association algorithm based on the cluster-based nearest neighbor standard filter (CNNSF) combined with the general Hough transform (GHT) is proposed. First, a modified dynamic equation for Kalman filter (KF) based on the scatterer projected trajectory model is derived. Then the state vector and covariance for the new dynamic equation are initialized by utilizing the GHT. Finally, the complete scatterer trajectory matrix is achieved efficiently by utilizing the proposed trajectory association algorithm based on the CNNSF algorithm. Simulation results demonstrate the correctness of the dynamic equation and the effectiveness of the proposed algorithm.

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 (GAN) achieves great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real parts 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 input. A complex-valued 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. 1The 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.

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.

As an innovative concept of high-resolution and wide-swath system, the low-oversampled staggered synthetic aperture radar (SAR) can not only deal with the blind ranges, but also suppress range ambiguities and reduce the data volume. Due to the variation of pulse repetition interval (PRI), there will be echo pulse loss and nonuniform sampling in azimuth. Generally, the existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid, and then perform conventional focused processing. However, the accuracy of the resampling is limited, and the advantage of the nonuniformity over uniform sampling in terms of reconstruction performance is ignored, especially with low oversampling factors. In this paper, a reconstruction algorithm for low-oversampled staggered SAR is proposed based on the sparsity Bayesian learning in the presence of a nonlinear PRI variation strategy. To ensure that the blind range distribution, which depends on the PRI variation strategy, brings a superior reconstruction performance, we define a novel objective function and optimize a sequence of nonlinear PRI variation with genetic algorithm. On the basis of the optimized sequence, the proposed reconstruction algorithm performs the second-order keystone transform to achieve range curvature correction for nonuniformly sampled data in azimuth. Then, a nonuniform observation model is established. The sparsity Bayesian learning (SBL) using a hierarchical form of the Laplace prior is applied to reconstruct the focused images directly with the nonuniform sampling. Simulations and experiments on raw data generated in staggered SAR mode with low oversampling factors are performed to verify the effectiveness of the proposed method.

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.