In spaceborne inverse synthetic aperture radar (ISAR) imaging of space targets, the high-speed motion of the radar and target can make the intrapulse motion not negligible. It can cause the range dimension matched filtering error, resulting in image defocusing. In traditional ISAR imaging scenarios, existing analysis and methods only focus on the high-speed motion of the target, without considering the radar, which is not suitable for spaceborne ISAR imaging of space targets. In this letter, the intrapulse motion of spaceborne ISAR imaging of space targets is analyzed in detail, and the corresponding compensation method based on modified phase difference (MPD) is proposed. First, the specific expression of the signal’s time delay considering the intrapulse motion is derived. Then, the range-dimension matched filtering error is analyzed, and the condition of ignoring the intrapulse motion is also obtained. This condition can be used to judge whether the intrapulse motion can be ignored in practical processing. According to the error analysis, an intrapulse motion compensation method based on MPD is proposed. This method does not need to estimate the motion parameters of the radar and target. The coefficient used to construct the compensation term can be directly estimated from the echo signal. Finally, the corresponding spaceborne ISAR imaging method with intrapulse motion compensation for space targets is proposed. Isolated scatterer and satellite electromagnetic calculation data experiments using the orbital data verify the effectiveness of the proposed method.

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.

Synthetic aperture radar (SAR) image change detection plays a crucial role in monitoring environmental changes and landform evolution. However, existing methods often struggle to retain details while reducing computational cost, or lack the ability to effectively incorporate global information, especially under complex and noisy conditions. To address these challenges, we propose a novel global robust structured graph approach with difference-aware filtering (GRSG-DAF), which effectively enhances the detection of changed areas by combining pixel-level information with the advantages of the superpixel-based structured graph. First, we construct a structured graph by integrating difference information, utilizing superpixel co-segmentation and adaptive affinity weighting, thus significantly reducing computational complexity and preserving critical structural patterns. Second, an image reconstruction process is implemented, utilizing a feature propagation mechanism to improve the contextual representation of the image. Finally, a difference-aware guided filtering process is developed by integrating the inherent guidance from the original pixel-level image, preserving boundary structures and spatial details. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods on five benchmark datasets, achieving higher accuracy while balancing effectiveness and efficiency in change detection.

The synthetic aperture radar (SAR) image of a complex target can be approximated by the composite composition of typical scatter bodies, by segmenting the SAR target image, the complex target can be decomposed into a structural combination of multiple scatterers. Based on the above conclusions, this paper proposes to model the structural components of complex targets under high-resolution observation conditions and construct a complex target imaging model, decompose a large complex ship target into multiple scattering bodies, coupled structures and large distribution areas, enhance the structured feature of complex targets based on the a priori knowledge of the coupling characteristics and distribution characteristics of known scatterers, and achieve the modeling of complex targets. this paper proposes a SAR imaging model with a combination of structural and statistical features to provide a new technical way of utilizing the data effectively.

In a synthetic aperture radar (SAR) system, the three-dimensional rotation of ship targets in the presence of medium to high sea states could cause Doppler frequency shifts and image defocusing, and even the defocusing phenomenon is space-variant along the range direction would occur. These issues would adversely affect the subsequent interpretation of ship targets in SAR images. This paper proposes a refocusing method based on a phase error estimation network (PEE-Net) to address the refocusing problem of three-dimensional rotating ship targets. The proposed method transforms the defocused complex-valued SAR ship image into the range-Doppler domain, estimates the phase error by range unit using PEE-Net, and compensates for space-variant phase errors. To train the network in an unsupervised manner and avoid the challenging task of obtaining labeling samples of non-cooperative ships, the proposed method introduces the image entropy loss function based on the minimum entropy criterion.

To achieve High-Resolution Wide-Swath (HRWS) imaging, operated with variable Pulse Repetition Frequency (PRF), staggered SAR will be applied to the next-generation spaceborne SAR system. Variable PRF will lead to non-uniform azimuth sampling. To demonstrate the effects of non-uniform sampling on the sea surface and moving targets imaging, a frequency-domain simulator is proposed in this paper. First, the echoes in staggered mode with nonuniform sampling in azimuth are generated by the frequency domain simulator. Then, the raw signal is reconstructed and resampled by the Best Linear Unbiased (BLU) interpolation, and focused by SAR imaging methods. The simulation results verify the effectiveness of the simulation process.

Integrated sensing and communications (ISAC) technology is an important way to solve the shortage of spectrum resources. Aiming at the problem of orthogonal frequency division multiplexing (OFDM) signals to be susceptible to the Doppler shift generated, which leads to severe degradation of the ISAC system performance based on OFDM, this paper proposed a new ISAC system based on orthogonal chirp division multiplexing (OCDM) signals. According to this system, not only we can implement the detection of moving objects accurately with OCDM signals, but also we obtain a better range resolution and a lower bit error rate (BER) under multipath channels.

Change detection (CD) methods for remote sensing images based on deep learning have garnered increasing research attention. However, existing deep learning approaches are often tailored for specific types of sensors. Extending these methods to dual-sensor scenarios presents challenges, including difficulties in data fusion and an increase in parameter numbers. To address these challenges, we propose a novel dual-stream encoder–decoder CD network architecture. In the encoder, the architecture comprises a shared-weight Siamese Unet stream for each sensor, with unique weights for different sensors. Before the decoder, a 3-D attention module (3-D AM) is incorporated, processing encoder outputs and fusing features from different streams. In addition, to mitigate the increased model parameter numbers due to the use of dual sensors, we propose a lightweight Unet architecture along with a time-difference structure in each stream. The proposed model is evaluated across multiple scenarios on a dual-sensor CD dataset, yielding an F1 score of 0.572 and the parameter number of 0.91 M. These results showcase high performance on a cost-effective level. Our code is available athttps://github.com/CodeofHuang/SmaDS_SiamUnet.

In recent years, researches on the recognition for Inverse Synthetic Aperture Radar (ISAR) images continue to deepen, while most methods only use the amplitude information of the ISAR image data. Besides, high-order terms in the complex-valued (CV) received signals for maneuvering space targets will cause defocusing on the ISAR images, which affects the accuracy of the recognition. For a steadily rotating maneuvering target, its high-order phase information between frames is relevant, and this information can be used to facilitate recognition. To this end, this letter proposes an end-to-end recognition framework in the CV domain based on the transformer model. It uses multi-scale feature extraction strategy and CV attention mechanism to get the local and global hybrid feature. Besides, A spatio-temporal transformer (STT) block is proposed to obtain the spatio-temporal correlation between image frames to assist recognition. Finally, a residual CNN block is introduced to promote diversity in the captured representations. In the experimental part, the recognition results of the proposed method on the real and simulated dataset are better than those of other methods. Compared with the classic sequence recognition method CVLSTM, the recognition accuracy and kappa coefficient of the proposed method are increased by approximately 5.6% and 5.4% respectively.

It is a challenging task to design a passive jamming method capable of both high fidelity and flexibility against synthetic aperture radar (SAR) imaging. Motivated by recent advances in the electromagnetic metasurface technique, a novel subsection-shift-Doppler-frequency jamming method based on the phase-tunable metasurface is proposed for the SAR system. Inspired by the realistic interference effects of the shift-frequency and segmented modulations, the proposed method flexibly imposes the subsection-shift-Doppler-frequency modulation on the SAR echo by the phase-tunable metasurface loaded with varactor diodes, which is designed to realize the continuous and real-time phase modulation through an external control system. Consequently, multiple false targets with flexible and controllable azimuth positions and verisimilar topological structures are generated instead of the protected target after receiving and processing the jamming signal by the victim SAR system. The influence of the phase-tunable metasurface modulation parameters on the jamming imaging effect is derived in theory. Simulations and experiments on satellite RadarSat-2 data are performed to verify the effectiveness of the proposed method. The theoretical analysis and experimental results show that the proposed method achieves the jamming imaging performance with high fidelity, high flexibility, low zero power, and low cost, effectively combining the advantages of traditional active and passive jamming.

Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) could provide a wide observable area and short revisit times while offering a moderate spatial resolution. The combination of maneuvering platform SAR receivers and MEO SAR transmitters has become a new research hotspot. However, the receiver often works in nonlinear trajectory with high maneuverability and highly squinted angle in practice. The curved trajectory of MEO SAR and the complex motion of the receiver would cause severe azimuth variance and range-Doppler coupling. The existing imaging algorithms mostly suffer from azimuth defocusing if applied to MEO/high maneuvering platform receiver bistatic SAR (MEO/HMP-BiSAR). To solve these problems, this article proposes a modified extended azimuth nonlinear chirp scaling (EANLCS) algorithm with a new perturbation function. A high-precision echo model is deduced for the modified EANLCS algorithm to describe the range history and echo phase. The modified EANLCS algorithm not only solves the problem of range cell migration and severe range-Doppler coupling, but it also equalizes the spatial variance of Doppler frequency modulation (FM) rates as well as high-order Doppler coefficients in MEO/HMP-BiSAR. The proposed algorithm markedly surpasses conventional algorithms in image quality. Simulations and real experiments are conducted to verify the effectiveness of the proposed algorithm.

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.

Ship targets have complicated motions such as random swings that change with the waves, which makes the target defocused and azimuth blurred in Synthetic Aperture Radar (SAR) images, and the classification accuracy of three-dimensional rotating ship targets is low. This paper proposes a mixed-type complex-valued convolutional neural network (Mix-CV-CNN). Mix-CV-CNN can make full use of the amplitude and phase information of complex SAR images, and can better complete the classification of SAR three-dimensional rotating ship targets without refocusing the target. Through SAR three-dimensional rotating ship target simulation analysis and actual measurement data verification, the proposed complex-valued convolutional neural network is experimentally analyzed. The superiority of the network improves the accuracy and reliability of SAR three-dimensional rotating ship target classification.

Aiming at the three-dimensional rotation speed estimation task of a synthetic aperture radar (SAR) ship target, this paper proposes a complex-valued convolutional long short-term memory (CV-ConvLSTM) network. It can simultaneously perceive the time, space and frequency domain information of the complex SAR imagery sequence. All elements of convolutional long short-term memory (ConvLSTM) network including convolutional layer, activation function, input gate, forget gate, and output gate are extended to the complex domain. In order to verify the superiority of CV-ConvLSTM in frequency domain information perception over ConvLSTM. Experiments show that CV-ConvLSTM has higher estimation accuracy than ConvLSTM.

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.

Synthetic aperture radar (SAR) system is susceptible to electromagnetic jamming during imaging, which seriously affects the subsequent interpretation of SAR images. Aiming at the problem of active suppressive jamming, this paper proposes a suppression method of SAR suppressive jamming based on self-supervised complex-valued deep learning, which consists of a novel complex-valued jamming suppression network CV-MUNet++ and a self-supervised training strategy. CV-MUNet++ could fully use the amplitude and phase information of complex-valued SAR images. The network’s weights, activation functions, and convolution operations are designed for complex domain processing. The different information representations of target and jamming in amplitude and phase in SAR images are mined to achieve jamming suppression. The self-supervised training strategy is proposed to solve the problem of relying heavily on manually labeled samples in the traditional network training process and is suitable for application scenarios where ground truth is difficult to obtain under complex jamming. The experimental results show that the proposed method could effectively suppress the active jamming of complex backgrounds and has the ability to self-supervised intelligent jamming suppression.

Since the synthetic aperture radar (SAR) images of internal wave contain additive and multiplicative noise under the ocean background, it is necessary to extract the internal wave to obtain the parameter information. This paper proposes a variational mode decomposition (VMD) method for separating and extracting internal waves. To solve the ambiguity in the estimation of direction, normalized radon transform in the elliptic domain is combined with the VDM, which can estimate the propagation direction of the internal waves accurately. Then performing experiments on Andaman Sea internal wave data collected by ERS-1 shows that VMD is least disturbed by the background information, and the estimation result of wavelength is closest to the measured value. At the same time, correct results are obtained by using the proposed method to estimate the propagation direction of the measured data from various sea areas, which proves the effectiveness of the proposed method.

In Synthetic Aperture Radar (SAR) images, ship targets suffer from blurring due to pitch, yaw, and sway, resulting in poor recognition accuracy. This paper proposes a complex-valued convolutional neural network (CV-CNN) architecture called CV-RotNet. This neural network could realize the recognition of SAR defocused ship targets without three-dimensional rotation refocusing. CV-RotNet makes full use of the amplitude and phase information of SAR images. Based on the classic deep learning architecture, RotNet and CV-RotNet were designed from the real domain and the complex domain. CV-RotNet and RotNet are tested on the five types of SAR three-dimensional rotating target simulation samples and the three types of GF-3 real ship target samples. Experimental results show that the average accuracy of CV-RotNet is higher than RotNet with the same degree of freedom, which reflects the advantages of CV-RotNet over RotNet.

Heterogeneous images imaged by different types of sensors have different imaging mechanisms, reflecting the characteristics of different sides of the target scene; while multi-source images formed by different working platforms or at different times have different imaging perspectives, and provide different target scene information. The use of multi-source heterogeneous images for fusion to obtain target and scene information more accurately and comprehensively has potential important applications in many fields such as military, medicine, and meteorology, and has become an important branch of image processing research. To this end, a PCGP algorithm is proposed in this paper to realize the fusion of optical images from different sources and SAR images. It first applies PCA transformation to the multi-source data images to obtain the principal component variables, then performs histogram matching on the first principal components of the transformed data sources, and finally uses the gradient pyramid decomposition algorithm to fuse the matched images to obtain a fused image. Then, the proposed fusion algorithm is tested in the fusion task of remote sensing images from different sources of GF2, GF6 and GF3. The experimental results show that the proposed fusion algorithm has better results.

With the rapid development of GNSS(Global Navigation Satellite System), reflected signal processing is applied in various fields. In this paper, the GNSS-R system is used to detect the moving target by compensation. Corresponding algorithms are proposed for medium RCS target and low RCS target respectively to improve the detection performance of the whole system. It lays the foundation for the follow-up target detection. Simulation results can prove the detection performance of the algorithm, and also demonstrate the feasibility of the GNSS-R system to detect moving targets from the perspective of simulation.

The significant curved characteristics of the medium-Earth-orbit (MEO) SAR systems lead to the 2-dimensional spatial variation, which brings difficulties to the large scene imaging. To solve this problem, this paper proposes a modified chirp scaling algorithm on MEO SAR (MCSoM). The algorithm is based on the idea of two-step azimuth space variation compensation. First, the bulk azimuth variance is removed in the range processing stage, and then combined with CS the algorithm completes the range cell migration (RCM)correction, and finally removes the remaining azimuth space variation in the azimuth processing stage. The proposed algorithm compensates for both RCM and the azimuth modulation term. Combined with the CS operation, the large-scene focused imaging of the MEO SAR can be realized. Simulation results validate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR) images, ship targets are defocused due to three-dimensional rotation, which affects subsequent SAR target detection and recognition tasks. This paper proposes a complex-valued convolutional neural network (CV-CNN) structure called CV-RefocusNet to refocus SAR three-dimensional rotating ship targets. CV-RefocusNet includes two parts of feature extraction network and image reconstruction network and adopts an end-to-end design method. To make full use of the amplitude and phase information of complex SAR images, the convolutional layer, deconvolutional layer, and activation function in CV-RefocusNet are all extended to the complex domain. Then refocusing experiments on simulated SAR data and GF-3 SAR data show that CV-RefocusNet could further improve the focus accuracy instead of real-value CNN (RV-CNN) with the same degree of freedom.

In MEO spaceborne-airborne BiSAR systems, the velocity and slant range histories are very different between the receiving and transmitting platforms. Under the condition of a large slant angle, the echo has serious spatial variability in the azimuth direction, which causes the azimuth defocus. In this paper, the bistatic SAR geometric models of satellite and aircraft are firstly established. After giving the range model, the pattern of spatial variation in azimuth dimension was analyzed. The nonlinear chirp scaling algorithm was used to correct the spatial variation, and the effectiveness of the proposed algorithm is verified by simulation.

The automatic registration of multi-modal remote sensing data (such as optical and SAR) is a challenging task because of the significant non-linear radiation difference between these data, as well as the transformation of rotation and scaling. In order to solve the above problems, this paper proposes a two-step strategy. Firstly, the improved HOPC algorithm is introduced to obtain matching point pairs, and then a weighted strategy is used to calculate the global affine transformation matrix. In the first step, an improved Harris operator is used to extract the points of interest, and the matching accuracy is maintained while the amount of calculation is reduced. Then the matching loss calculated by the HOPC algorithm is used to calculate the contribution weight of the image block, which is utilized to calculate the global transformation matrix. The results show that the method in this paper has strong robustness to complex nonlinear radiation differences, and the two-step strategy greatly improves the accuracy of matching, which is better than similar matching algorithms in performance.

Multiple input multiple output synthetic aperture radar (MIMO SAR) can solve the high resolution and wide swath imaging conflicts of traditional SAR. In the time domain, short term shift orthogonal (STSO) waveforms of different transmitting channels were used to separate the echoes from closely spaced scatterers. And in the space domain, digital beam forming (DBF) was used to separate the echoes from widely spaced scatterers. In this paper, a novel phase coding scheme based on Golay complementary sequence is proposed. Based on the idea of STSO waveforms, phase coding scheme could be used to separate and image the echoes of different receiving antennas. Theoretical derivations and simulation results validate the effectiveness of the proposed coding scheme.

This letter proposes a complex-valued convolutional gated recurrent unit (CV-ConvGRU) network for the three-dimensional rotation refocusing task of a synthetic aperture radar (SAR) ship target. To take advantage of the amplitude and phase information of complex SAR images, all elements of CV-ConvGRU, including the convolutional layer, activation function, update gate and reset gate, are extended to the complex domain. Based on CV-ConvGRU, a complex-valued SAR ship refocusing network (CV-SSRN) architecture is designed for refocusing experiments. To verify the robustness of the proposed CV-ConvGRU over ConvGRU on information perception, this letter also raises a real-valued SAR ship refocusing network (RV-SSRN), which has the same degree of freedom as CV-SSRN. Finally, experiments are carried out, and all results show the superiority of the proposed method on refocusing accuracy.

Strong reflections from the marine surface reduce the contrast between the target-of-interest and the background in synthetic aperture radar (SAR) images and severely affect the interpretation of the image. This letter proposes a framework of SAR sea clutter suppression based on a new self-supervised training strategy referred to as Clutter2Clutter (C2C), which mines self-supervised information from a large number of unlabeled SAR patches for network training. This letter also proposes a complex-valued UNet++ (CV-UNet++) network model to make full use of both amplitude and phase information of the complex SAR image, and the C2C strategy is used to train the CV-UNet++ for sea clutter suppression. Experiments on GF-3 and TerraSAR-X SAR data show that the proposed method has a better effect on suppressing sea clutter and is able to preserve the target-of-interest energy well.

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).

Recently, researches on the classification for inverse synthetic aperture radar (ISAR) images continue to deepen. However, the maneuvering and attitude adjustment of space targets will bring high-order terms to received echoes which cause defocus on ISAR images and affect classification. The current classification models ignore the information of high-order terms containing in the relationship of real parts and imaginary parts of data. To this end, this letter proposes an end-to-end framework, called CV-GNN, specifically for the classification of defocused ISAR images under the few-shot condition. It models the features of real parts and imaginary parts of complex-valued (CV) images as graph information reasoning. Specifically, the deep relationship between them is mined to contribute to classification by complex-valued graph convolution. Moreover, the backpropagation process is derived in detail for updating the weights and bias of the network. The proposed method is then experimented with a mixed few-shot dataset of real and simulated data. Compared with the state-of-the-art methods, CV-GNN performs well in defocused image classification for each class of targets, and ablation studies verify the effectiveness of complex-valued network and graph neural network. The code and dataset will be available online (https://github.com/yhx-hit/cv_gnn).

For the target with 3-D rotation, the 3-D synthetic aperture radar (SAR) imaging is very important for the feature extraction and classification. To solve this issue, a novel 3-D imaging algorithm through the airborne SAR system with a single antenna is proposed in this article, which has great advantages for the simplification of system structure compared with the traditional interferometric system. The proposed 3-D airborne SAR imaging algorithm can be implemented with three steps: 1) the azimuth signal is modeled as multicomponent linear frequency modulation (LFM) signal due to the relative movement between the target and radar. 2) The scatterer height position can be obtained by estimating the frequency modulation rate (FMR) for the LFM signal. 3) The 3-D airborne SAR image is obtained via the range-Doppler (RD) algorithm. Furthermore, the reconstruction performance under different rotation patterns, including roll, pitch, and yaw, is analyzed. The availability of the presented novel technique is demonstrated by the results of simulated and real experimental data.

For the convenience of target feature extraction, classification, and recognition, it is necessary to research the imaging projection plane (IPP) of spaceborne hybrid synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging results of ship target on the sea surface. In this article, a novel analysis approach of IPP is proposed, by which the expressions of IPPs can be derived under the condition of different rotation and ship’s bow directions. Then, the representation of radar image can be predicted in advance, i.e., top view, side view, or hybrid view of the target. Finally, the simulated and real measured data are processed to generate the radar images to verify the correctness of conclusions in this article.

Motivated by recent advances in deep learning, a novel deep complex-valued convolutional neural network (CV-CNN)-based method is proposed for ground moving target indication (GMTI) in a multichannel synthetic aperture radar (SAR) system. The proposed method integrates the SAR-GMTI task into a blind inverse problem solved by a deep CV-CNN named CV-GMTINet. To take advantage of the amplitude and phase information of complex multichannel SAR images, both feature maps and network parameters are extended into the complex domain. The proposed CV-GMTINet is designed by adopting complex-valued residual dense blocks (CV-RDBs) to adaptively learn complex hierarchical features. The trained CV-GMTINet, as a GMTI processor, can be applied to complex multichannel SAR images to discriminate moving targets from stationary clutter and refocus the moving target images simultaneously. Experiments on TerraSAR-X data show that the proposed method achieves significant improvements over existing state-of-the-art GMTI methods in both detection performance and refocusing accuracy, especially for the slow-moving target and the moving target with only along-track velocity.

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.

The echo of maneuvering targets are analyzed in this paper, which combined with Polynomial phase signals (PPSs), and compared the parameter estimation performance of PPSs based on the fractional Fourier transform(FRFT), the cubic phase function and the product cubic phase function(PCPF) for the echo characteristics. In the third section of the paper, sparse aperture imaging is performed on the aircraft, which flies smoothly and the echo is randomly missing, and the RD algorithm of the maneuvering target is compared with FRFT and PCPF algorithm.

With the cross of Global Navigation Satellite Systems (GNSS) technology and other subject, the application of GNSS has been expanded. This paper focus on the research on the feasibility investigation of target detection with GNSS and a stationary receiver. The system is discussed as a whole and performs link calculation to verify the experimental scene. The experimental results obtained from the GNSS and stationary receiver experiments are presented and analyzed to demonstrate the performance of the algorithm.

This paper mainly studies the airborne SAR imaging of moving ship under low sea state, and proposes a method to estimate ship velocity and achieve focused imaging. Moving ships may cause range migration, Doppler frequency shift and chirp rate changes in slow-time dimension, which make image defocused. A refocusing method is proposed based on correlation Doppler estimator, Second Order Keystone Transform (SOKT) and integrated cubic phase function (ICPF) to deal with these problems. First, linear range walk and linear phase are compensated with correlation Doppler estimator. Then, range curvature and quadratic phase are eliminated with SOKT and ICPF, respectively. With these estimated parameters, we can solve the velocity of the ship and obtain the focused image. The processing results of simulation and real data both verify the algorithm.

In the synthetic aperture radar (SAR) images, moving ship targets are defocused due to the movement, which leads to the problem of poor classification accuracy. Therefore, this paper proposes an amplitude-phase-type complex-valued convolutional neural network (AP-CV-CNN) architecture called CV-MotionNet to classify SAR moving ship targets without motion compensation. It utilizes both amplitude and phase information of complex SAR images. CV-MotionNet uses amplitude-phase-type activation function to processing amplitude and phase information more conducive. Then, the proposed CV-MotionNet is tested on simulated five-types SAR moving ship target classification task and GF-3 SAR ship classification. Simulation and experiment show that the classification error can be further reduced if using CV-MotionNet instead of real-valued CNN (RV-CNN) with the same degree of freedom.

Imaging of ground multiple moving targets in a synthetic aperture radar (SAR) system is a challenging task due to the fact that targets are defocused owing to motions and contaminated by the strong background clutter. Motivated by recent advances in deep learning, a novel deep convolutional neural network (CNN)-based method, DeepImaging, is proposed for ground moving target imaging (GMTIm). Different from conventional imaging methods relying on the prior knowledge of imaging, the proposed DeepImaging is directly trained to learn an implicit imaging model of multiple moving targets. It is free of motion parameter estimation and iteration process. Then, the trained DeepImaging, as an imaging processor, can be applied to the SAR complex received data after clutter suppression to achieve the multiple moving target imaging and the residual clutter elimination simultaneously. Simulations and experiments on the Gotcha data show that the proposed method achieves significant improvements over existing state-of-the-art GMTIm methods in terms of imaging quality and efficiency.

Global Navigation Satellite Systems have provided opportunities for passive synthetic aperture radar. This paper presents bistatic synthetic aperture imaging preliminary results with GNSS transmitters and a low-orbit satellite receiver. General shift geometry, improved imaging method and coverage analysis of multi-GNSS and low-orbit satellites are discussed to prove the capability and challenges of the system. The obtained results are given to demonstrate the performance improvement of GNSS-BiSAR imaging.

The recognition of satellite's attitude change plays an important role in the detection, tracking and recognition of space targets, as well as the evaluation, verification of space events and environmental monitoring and prediction. In this paper, 3D-CNN model is used to extract features from spatial and temporal dimensions, and then 3D convolution is carried out to capture motion information from multiple consecutive frames. Four common attitude changes of three different kinds of satellites are simulated, which are orbit change, spin, reconnaissance and maneuver. A proper number of consecutive frames are sent into packets and sent to the network for training. The experimental result shows that 3D-CNN model has a competitive performance.

In this paper, a variable-decoupling method for synthetic aperture radars (SAR) with three-dimensional constant acceleration is proposed. For SAR with constant acceleration, a high-order approximate slant range model is re-established. On this basis, the spectrum is obtained by the method of series reversion (MSR) that has five independent variables coupling, which makes the accurate processing of the spectrum difficult. Aiming at this problem, a variable-decoupling method based on Doppler centroid estimation and inertial navigation data is proposed for SAR with 3-D constant acceleration and MSR-based spectrum. Combined with the improved chirp-scaling algorithm (ICSA) which is based on the MSR-based spectrum, an improved wide-range imaging result is obtained.

The multi-Rotor UAV SAR (MRUAV-SAR) platform has a very low flight altitude and poor stability performance, and its trajectory is easily affected by airflow. The low velocity of the carrier platform makes the effect of phase errors greater. This paper first models the phase error of MRUAV-SAR and analyzes the impact of the phase error on the imaging results. Then introduced an estimation and compensation method based on the quadratic phase error and the cubic phase error. Phase gradient autofocus algorithm (PGA) is used to compensate for higher-order phases. Finally, the point target was used for simulation verification, and the results of processing the measured data were compared and analyzed.

Deep learning method is widely applied in modern artificial intelligence technology for Signal Modulation Recognition (SMR). Compared to CPUs and GPUs, FPGAs are highly energy-efficient and have low-latency streaming capabilities, which are more suitable for energy-sensitive or real-time machine learning projects. High-level synthesis (HLS) can automatically convert the logical structure described by a high-level language into a description by a low-level abstraction language. In this paper, we propose a system to optimize Deep Confidence Network (CDBN) by loops pipelining and unroll, memory buffering and partitioning, and implement an energy-efficient HLS-based FPGA Convolutional CDBN accelerator for SMR based on Virtex-7 platform. The accelerator system run at 150MHz and has 28% higher throughput and 80.5% less power consumption than a GPU implementation.

Nowadays, deep learning has got a major success in computer vision, especially in image recognition. In this paper, a new architecture based on DenseNets which is referred to as Multi-Scale Input Spatial Pyramid Pooling Fusion Networks (MSPPF-nets) is proposed for the work of classification of local climate zones (LCZs). Multi-scale remote sensing images can be inputs of the networks by the benefit of Spatial Pyramid Pooling (SPP) layer, multi-scale features from different channels were extracted and fused by our multi-branch-input framework. The final classification results have illustrated the feasibility of this presented classification method.

With the successful application of convolution neural network (CNN) in image recognition field, this paper presents the complex-valued convolutional network (CV-CNN) using different activation functions. Then, four different activation functions of sigmoid, tanh, Leaky-ReLU and ELU were tested in typical polarimetric SAR image classification tasks. Experiments on benchmark datasets of Flevoland shows that CV-CNN using ELU activation function performs the best, with faster convergence speed and much higher recognition rate.

This paper presents bistatic synthetic aperture radar imaging preliminary results with multiGNSS transmitters and a fixed receiver. Geometry, ambiguity function and imaging algorithms are discussed to describe multi-GNSS BiSAR system. Simulation results are provided to demonstrate the performance improvement of the amount of information of a given scene.

Aircraft detection has become a research hotspot due to its important military and traffic status. It is very challenging since noisy background is easy to mix with the target, meanwhile, there are small and dense distributed targets in some images. This paper presents an end to end aircraft detection framework based on Mask R-CNN. First, a self-attention feature pyramid network (SA-FPN) is proposed to suppress the noise and highlight foreground. Then, in order to reduce the false alarm and increase the detection performance, we redesign the aspect ratios of the anchors. The experimental result shows that our detection method has a competitive performance.

A new moving target tracking method based on an improved Gaussian mixture probability hypothesis density (GMPHD) filter in a circular synthetic aperture radar (SAR) system is proposed. In order to obtain the temporal dynamic behavior of moving targets in a SAR scene, a multiple-target observation is first extracted from a sequence of clutter-suppressed subaperture circular SAR images. Then, a new multiple-target tracking model for the circular SAR system is established based on the dynamic behavior of moving targets. An improved labeled GMPHD filter with an adaptive target birth intensity is proposed to realize the moving target detection and tracking in a strong ground clutter environment, which is suitable for the circular SAR system. This method can not only provide accurate estimates for the number and states of moving targets but also can extract their trajectories. The effectiveness of the proposed method is verified through simulations and experimental Gotcha data.

Windmills have the characteristics of small area and small quantity in remote sensing images, so the traditional methods of object classification and recognition are not suitable for the recognition of windmills. In this paper, we analyzed the spectral information and shape characteristics of windmill, and proposed a technique of recognition windmills in remote sensing images based on SVM (support vector machines) and morphological attribute filters. The main idea of technique can be parted into two steps: the remote sensing image are divided into windmill and windmill-like areas, using morphological attribute filters to filter out the windmill-like areas. In addition, we have recognized the distributed windmills group in the images of four regions, and verify the accuracy of the recognition technique.

In this paper, a novel moving target detection and tracking approach is presented based on Gaussian mixture probability hypothesis density (GMPHD) filter in synthetic aperture radar (SAR). Based on the advantages of GMPHD filter and the characteristics of moving target in SAR imagery, GMPHD filter is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets in heavy clutter environment. Utilizing the tracking algorithm, the states and the number of moving targets are obtained over time. Simultaneously, the strong ground stationary clutter lacking of the dynamic behavior is eliminated finally. Both simulation and real Gotcha data set processing results are provided to demonstrate the effectiveness of the proposed approach.

Fast Moving targets always are shifted or smeared outside the scene in different images sequence to make video by Circle Synthetic Aperture Radar (SAR). In this paper, a novel moving target tracking approach with the shadow detection and tracking (SDT) is presented based on Convolution Neural Network. Based on the shadow characteristic of moving target in SAR imagery, CNN tracking classification is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets. By the simulation experiments and performance analysis, the validity of the proposed algorithm can be demonstrated. Real data set processing results are provided to demonstrate the effectiveness of the proposed approach.

In the SAR image, the road recognition is conducive to the SAR image interpretation using in traffic monitoring, GIS information, and Geographical mapping, but the road is difficult to extract for blurring by speckles. Based on mathematical morphology, a new road extraction method is proposed in this paper. After the pre-processing to reduce the influence of the speckles, the binary image information is gained by the Otsu method. Then, the information without the road information is removed by the mathematical morphological opening operation. In addition, after being corroded and reconstructed, the complete road boundary is detected by edge detection operations, and the result is relocated on the original image. Compared with the morphological method, road extraction is done by applying the Zernike moments. Orthogonal property of Zernike moment basis functions guarantees the statistically independence of coefficients in extracted feature vectors. The proposed method is evaluated on the SAR Image data. Results show that presented method outperforms recently presented works due to its high performance.

Based on the fact that the moving targets are sparse in spatial domain after clutter suppression, we present a novel approach for the indication of moving targets with compressive sensing from dual-channel spaceborne SAR sparse raw data. Moreover, the detection performance measures in terms of GMTI are different from the existing ones for CS. In this paper, the probability of detection (PD)-curves are presented to analyze the detection performance of different CS reconstruction approaches. Not only the experiments with the dual-channel spaceborne SAR simulated and real data but also the detection performance of the proposed method is analyzed in this paper. The result shows that this method is more effective.

Geosynchronous (GEO) SAR is a radar satellite running in the inclinational geosynchronous orbit (36000km). During the long signal propagation delay time, the relative motion between satellite and target cannot be ignored. In this paper, the two-way slant range is analytically obtained from vector derivation. Meanwhile, it is found that the image formation must consider the non-uniform velocity in a long synthetic aperture time, and therefore, based on the derived slant range and the instantaneous velocity of satellite, a modified back projection algorithm is put forward to overcome the error of the “stop-and-go” error as well as the effects of the non-uniform satellite velocity. Simulation results validate the correctness of the improved imaging algorithm.

It presents an approach of moving target detection by multichannel synthetic aperture radar (SAR), which the receivers displaced in heterogeneous arrays. The approach achieves the maximal unaliased band of radial velocities and Minimum detectable velocity, retains full resolution SAR images, the multiple measurement vectors were employed for improvement of the target focusing, and requires no increase in receiver samples. The proposed multi-channel synthetic aperture radar system and the associated signal processing are detailed, and the approach is numerically demonstrated via simulation and raw data experiments.

Geosynchronous synthetic aperture radar (GEO SAR) data focusing is a more challenging and difficult task than the low earth orbit (LEO) SAR due to the strong 2-D coupling of echo signal induced by the orbital trajectory curvature. The range cell migration (RCM) in the GEO SAR configuration is space variant in both range and azimuth directions, hence standard RCM correction (RCMC) functions developed for LEO SAR are inadequate for GEO. In this letter, a curved trajectory model is proposed, taking into consideration the impacts of “stop-and-go” assumption. Based on the range model, a new data transform is derived to deal with the complicated coupling in GEO SAR. From the derivation, we find that the original Stolt mapping is a special case of the proposed “generalized Omega-k” algorithm. In comparison with the original Omega-k algorithm, this new algorithm can correct more complicated RCM effectively. Finally, simulation results show that the proposed imaging algorithm performs well for large scene focusing in an L-Band GEO SAR system.

In order to detect and imaging the moving boat monitoring ships from the dynamic ocean surface correctly in the image of Synthetic Aperture Radar, a novel approach based on fractional Fourier Transform with the Dual Receive Antenna SAR data was introduced. The traditional method of motion parameters estimation and focusing was modified, and the processing and analysis to improve the performance of estimation of FM rate introduced by the along-track moving of the boat, the time-frequency distribution was provided and the accuracy estimation by Fractional Fourier Transform was given, then the refocusing method was proposed. The experimental result shows that the proposed method is very effective.