Hyperspectral anomaly detection is a crucial technique for recognizing abnormal pixels in hyperspectral images (HSIs), that is, those with distinct spectral characteristics from those of the surrounding background. Traditional methods always fall short in effectively leveraging the information regarding the spectral and spatial aspects of the dataset simultaneously, limiting their detection performances. This article proposes a novel framework using U-Net, termed hybrid convolution and transformer-based U-Net (HCT-Unet), which integrates convolution with a multihead attention mechanism in Transformer for enhanced hyperspectral anomaly detection. To ensure a more comprehensive understanding of spatial and spectral interactions, the HCT-Unet architecture capitalizes on the strengths of local feature extraction of convolutional layers and the capabilities of the long-range dependency modeling of Transformers. A key innovation of this framework is an error attention mechanism, which facilitates adaptive multiscale feature fusion and enhances the feature representation capacity. Furthermore, a new anomaly score calculation method is proposed, which combines reconstruction error with the pixelwise structural similarity index (SSIM) to determine pixel anomaly from both local structural preservation and global spectral consistency perspectives. Experiments carried out on seven different hyperspectral datasets reveal that the proposed method consistently outperforms the widely accepted state-of-the-art methods in hyperspectral anomaly detection.

Compressive sensing (CS) theory provides a positive contribution to ISAR imaging. However, the imaging performance of Compressive Sensing Inverse Synthetic Aperture Radar (CS ISAR) imaging methods is limited by the sparsity of the target scene. Dictionary learning (DicL) has been incorporated into CS ISAR imaging better to sparsify the target scene in a certain domain and improve imaging performance. However, the existing dictionary learning-based CS ISAR imaging methods are not adaptive enough and time-consuming. The algorithm parameters need to be manually adjusted for different targets. Their common iterative structure leads to relatively low computational efficiency. To improve the adaptive ability and computational efficiency of DicL-based CS ISAR imaging, we propose an enhanced convolutional DicL-based CS ISAR imaging method. Apart from exploiting the strong learning ability of the multi-layer network structure offered by the convolutional DicL, an attention mapping inferred in both spatial and channel dimensions and a multi-branch convolution are incorporated to enhance the sparsity in the latent space of the Convolutional DicL in CS ISAR imaging. The quantitative and qualitative analyses of the experimental results show that the proposed CS ISAR imaging method outperforms the existing DicL-based CS ISAR imaging methods and is also superior to the typical model-driven DL-based methods like ADMM-net.

The registration of synthetic aperture radar (SAR) and optical images is critical in multimodal remote sensing image fusion. In recent years, deep learning-based registration networks have been continuously introduced. However, owing to the significant disparities in viewing angles and radiometric properties between SAR and optical images, current deep learning methods struggle to fully exploit the physical properties of radar imaging. In addition, many existing matching networks typically perform only a forward pass, resulting in suboptimal model performance. This article proposes a cyclic iterative registration SAR mechanism network (termed as CIRSM-Net) for the registration of SAR and optical images. First, we design a learning module that integrates the radar equation with a microwave scattering model to capture deep features from SAR images, and design a corresponding scattering feature loss to aid in better generalization across various radar images. Then, to explore optimization methods for matching networks, this study proposes a strategy of multiple iterative optimizations within the matching network. Specifically, it integrates speeding-up radiation-variation insensitive feature transform (RIFT2) supervision in the backend matching network and iteratively optimizes the final output. Finally, during the iteration process, we propose an innovative matching loss function that combines the rotation invariance supervision of RIFT2 with iterative optimization techniques to enhance feature matching accuracy. Experimental results on both public and our own datasets additionally confirm the effectiveness and superiority of the proposed approach, demonstrating its significant potential for practical applications.

Waveform decoupling is usually considered a more technical challenge for fully exploiting the potential benefits provided by multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) structure. Spotlighted as a promising solution to this challenge, the well-known orthogonal-waveform beamforming scheme has become increasingly popular. However, in some cases, the performance of digital beamforming (DBF) involved in this scheme may be significantly degraded due to the nonlinear array configuration under stringent space constraints. Up until now, relatively little research on robust DBF on receive in elevation has been presented for a nonlinear array configuration. To alleviate this, we here propose a least absolute shrinkage and selection operator (LASSO) regression-based DBF technique for the improved segmented phase coding (SPC) decoupling scheme. First, a generalized steering vector formation model based on a 3-D geometric vector is provided for the subsequent DBF operation. Given that, due to the nonlinear array configuration, the calculated steering vector exhibits space-varying characteristics within the azimuth pulse extension of the illumination beam, steering vector constraints for the desired signal and interferences are subsequently designed to allow for such variation within the azimuth footprint. Furthermore, the beamforming problem is addressed by generalized LASSO regression to approach the goal that providing a distortionless response and deep nulls for each desired signal and interference component within the azimuth pulse extension. Finally, we assess the feasibility and performance of the proposed LASSO regression-based DBF technique for waveform decoupling with the use of numerical simulations.

The spaceborne multistatic synthetic aperture radar (SAR) system offers a flexible baseline that provides more observation angles and higher interferometry accuracy. However, any phase deviation among the independent oscillators in the spaceborne multistatic SAR system can cause a residual modulation of the echoes. Therefore, accurate phase synchronization is crucial for the system. The pulsed alternate synchronization scheme accurately extracts phase errors between different platforms, as verified in the TanDEM-X mission. Furthermore, an advanced noninterrupted pulsed alternate scheme uses the time interval of transmitting sequence to realize phase synchronization without interrupting the normal operation of the radar, which is verified in the LuTan-1 mission. However, with the increasing number of spaceborne multistatic SAR platforms, the time interval of the system may not be sufficient to support noninterrupted phase synchronization. To this end, this article proposes a novel phase synchronization method to improve the efficiency of phase synchronization for spaceborne multistatic SAR systems. First, a model for phase synchronization is built based on the pulsed alternate synchronization scheme, and the constraint between phase synchronization accuracy and waveform properties is analyzed in detail. Second, a quasi-orthogonal waveform optimization method, which can realize the rapid generation of phase synchronization waveform with better correlation properties, is introduced to improve the accuracy of phase synchronization. Third, to further reduce cross correlation energy between waveforms, we introduce generalized short-term shift-orthogonal (STSO) waveforms for phase synchronization. This waveform can achieve local orthogonality with a known baseline, improving the accuracy of phase synchronization greatly. Finally, the proposed method is verified through detailed simulations and ground experiments.

Bistatic synthetic aperture radar (SAR) with spatially separated transmitter (TX) and receiver (RX) is advantageous over monostatic SAR systems in trajectory flexibility and antistealth/antijamming capability. On the other hand, since bistatic SAR imaging involves more technical complexities and incurs higher cost, the research in the field of bistatic automatic target recognition (ATR) has been mainly relying on simulated SAR imagery. Reckoning with the lack of supporting database in the public domain, the researchers at Nanjing University of Aeronautics and Astronautics (NUAA) constructed a proprietary bistatic SAR database featuring multiple types of representative military vehicles with the self-developed miniSAR system. Moreover, a multichannel multiview feature fusion network (MMFFN) is devised by incorporating the vision transformer (ViT). The simulation results show that the proposed MMFFN offers a classification accuracy improvement of 4.86%–16.63% over the baseline network (i.e., the plain ViT) in a series of experiments featuring small-to-large observation angle deviations between the training and test data.

Real-time echo restoration from signals containing range ambiguities is a technique challenge for high pulse repetition frequency synthetic aperture radar (SAR). In this letter, to ensure real-time processing, a novel azimuth phase coding scheme is utilized to realize the processing of raw data in groups, which is conceived for a conventional SAR system. Meanwhile, an advanced fast algorithm is proposed to further decrease the computational complexity of the restoration processes. The proposed scheme is validated by the simulated point-like and distributed targets SAR data. The quantitative analysis results show that the ambiguous signal can be at least suppressed by 40 dB and the image average range ambiguity separation error is less than -60 dB. Finally, compared with some conventional methods in computational complexity and memory cost, the results illustrate that the processing efficiency has been significantly improved and the memory resource occupation has been significantly reduced. Particularly, the proposed fast algorithm improves the computational efficiency by about 80 times.

Military operations often demand considerable concealment and raid capabilities, particularly at night or in adverse weather conditions. However, the use of synthetic aperture radar (SAR) technology provides early warning and target localization capabilities. While spaceborne or airborne SAR systems can capture expansive SAR scenes, they frequently encounter challenges in delivering timely and high-resolution data, thereby limiting their effectiveness in detecting small ground vehicle targets. To address this issue, our research has developed a low-cost, high-resolution, and real-time monostatic MiniSAR system for the effective detection of small targets, such as vehicles. Furthermore, to enhance the stealthiness of the MiniSAR, a bistatic MiniSAR system has been developed to accomplish detection tasks. Nevertheless, despite the utilization of MiniSAR systems for ground armored target detection, two primary challenges persist: the presence of highly ultracomplex scene interference making accurate target detection difficult; and poor real-time performance resulting in slow detection and tracking. To overcome these challenges, this article proposes a ground vehicle target recognition method based on an improved lightweight anchor-free detection network using monostatic/bistatic SAR images. The method initially leverages the inherent features of SAR targets for localization, embedding these features into SAR images, and then outputs detection results through the improved lightweight anchor-free network. We validate the effectiveness of this method on our self-constructed monostatic/bistatic SAR datasets and verify the algorithm’s robustness on publicly available ship datasets. Experimental results demonstrate that this method outperforms other representative methods in detecting SAR vehicle small targets, exhibiting higher detection accuracy and timeliness.

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using segmented phase coding (SPC) waveforms. The proposed MSS-WS scheme can halve the number of interferences from far arrival angles, allowing for a reduction of the system complexity. In addition, constrained/generalized least absolute shrinkage and selection operator (LASSO) regression is exploited to form the beamformer with relatively high robustness in terms of dealing with the presence of topographical variations. The so-called LASSO-based dynamic beam response (LASSO-DBR) technique introduced here contains two parts: the source localization and the beamforming based on the designed constraint matrices. In this respect, the proposed LASSO-DBR beamformer can produce a distortionless response to the desired signal and still yield wide nulls for the unwanted interferences. Using numerical simulations, we illustrate the feasibility and performance of the proposed MSS-WS framework using the LASSO-DBR beamforming technique.

Pansharpening refers to the fusion between a multispectral (MS) image with abundant spectral information and a panchromatic (PAN) image with high spatial resolution to obtain a high spatial resolution multispectral (HRMS) image. The traditional pansharpening methods often ignore the effect of path-radiation caused by scattering from different atmospheric components, and the few methods that introduce haze correction only calibrate each band of the MS image individually, without exploring the intrinsic correlation among different bands. To address this problem, low rank tensor completion pansharpening based on haze correction (LRTCP) is proposed. The haze-line prior is first introduced into the joint haze correction of MS and PAN images, and obtain the pre-modulated images with the help of the improved high-pass modulation (HPM) injection scheme. We then use tensor completion to simulate the degradation problem by applying low-tubal-rank tensor complementation to the process of reconstructing HRMS images, thus constructing a low rank tensor completion pansharpening model based on haze correction. Finally, the alternating direction multiplier (ADMM) is employed to find the solution of the proposed approach, producing the final fusion result. Comprehensive qualitative and quantitative assessment of reduced- and full-resolution datasets from different satellites shows that the proposed method outperforms the state-of-the-art methods.

The majority of the existing deep-learning based SAR automatic target recognition (ATR) algorithms rely solely on the "appearance" of the SAR signatures for target classification, while ignoring the relationship between the objects of interest and their surroundings. In this work, we emphasize on enhancing the capability of SAR ATR algorithms in detecting and categorizing out-of-library (OOL) targets in open environment with context-based compositional learning. Rather than attempting to build a single do-it-all model, task-specific sub-models are chosen based on the natural selection mechanism, whose relationships are structured via logic flow based on context. To compensate for the SAR training data scarcity and the unequal distribution of classes, active learning, cross-domain transfer learning, and transductive learning are jointly exploited. Simulation results show that the proposed joint scene-target recognition framework could potentially solve the challenging problem of OOL target classification in complex mission scenarios.

Multi-input and multi-output (MIMO) radar has drawn much attention in synthetic aperture radar (SAR) due to the possession of more degrees of freedom (DOFs). However, there are some repeated equivalent phase centers (EPCs) caused by the same wave path have no contribution to the improvement of DOFs. To this end, a novel interpulse phase coding and multi-carrier (IPCMC) transmission scheme is investigated to restored repeated EPCs. Furthermore, an advanced range ambiguity separation method is proposed based on the increased efficient EPCs. Finally, distributed targets simulation experiments are performed and the experiment results illustrate that the range ambiguity suppression performance is significantly improved due to the restored EPCs.

To address the echo separation issue involved in multiple-input multiple-output (MIMO) synthetic aperture radar (SAR), the elevation beamforming solution has become increasingly popular and been widely investigated. However, the current elevation digital beamforming (DBF) schemes usually require high hardware complexity, which is not allowed for practical MIMO-SAR systems. To alleviate this problem and achieve a low-cost MIMO-SAR system, we here detail an improved two-stage echo separation scheme, i.e., subband match filtering and DBF (SMF-DBF). First, the subband match filtering enables the number of interference components to be halved. Afterwards, the remained interferences from far arrival angles will be suppressed by DBF techniques. The two-stage processing can considerably simplify the array configuration and reduce the system complexity. Numerical simulations have demonstrated the feasibility and potential of the proposed method for channel-limited MIMO-SAR systems.

The echo separation problem in the application of multiple-input multiple-output (MIMO) concept on synthetic aperture radar (SAR) systems is considered as a more technical challenge. It has been shown that the separation of aliased signal returns can be achieved by the state-of-the-art elevation beamforming techniques, e.g., the well-known short-term shift-orthogonal (STSO) scheme. However, the directions of arrival (DOAs) of the signal segments are usually inaccurate due to the unknown topography variation, especially for airborne MIMO-SAR systems. The DOA mismatch can seriously deteriorate the digital beamforming (DBF) performance of STSO scheme. To this end, a covariance-matrix-reconstruction-based (CMRB) robust beamforming technique is introduced in our paper to alleviate the effect of DOA mismatch on echo separation. Extensive simulations that in comparison with the current DBF methods have been carried out to prove the effectiveness and prospect of the proposed CMRB DBF technique for airborne MIMO-SAR systems.

Waveform design plays a critical role in ruling the performance of a pulse compression radar system, and keeps being a hotpot for several decades. Unfortunately, some coded waveforms being widely employed in recent years nearly have their limitations. The idealistic phase code waveform has a high spectral sidelobe brought by the instantaneous phase change. The polyphase-coded FM (PCFM) waveform can provide a continuous phase function, but the frequency template error (FTE) metric is indispensable for it to control the spectrum, thereby inducing high design complexity. The nonlinear frequency modulated (NLFM) waveform has a controlled spectral content, while its coding structure is non-parameterized. To this end, we develop a novel parameterized frequency modulated (PFM) waveform and a constant envelope. Simulation and real experimental results verify the superior performance of the proposed waveform in term of autocorrelation sidelobes and energy ratio within bandwidth compared to the phase code and PCFM waveforms.

The missile-borne SAR works in the forward-squint state, and a higher flight speed will cause a very large relative radial velocity between the target and the antenna phase center (APC). The classic polar format algorithm (PFA) and its improved version do not effectively compensate for the intra-pulse Doppler history resulting from large radial velocity, which leads to defocused images. Therefore this paper proposes a modified version of the PFA, which can solve such image defocus. Compared with the classical PFA, the modified PFA has the same advantages of simplicity and efficiency while implementing higher image quality by compensation of the residual phases caused by large radial velocity. The modified PFA is suitable for hardware implementation, since only fast Fourier transform (FFT) and complex vector multiplications are required in the range dimension processing. In this paper, simulation examples are employed to verify the validity and advantages of the modified PFA.

Deep learning (DL) has succeeded in the field of target detection and has been introduced into the researches of ground moving target indication (GMTI) for synthetic aperture radar (SAR) recently. Due to the lack of labeled data in SAR/GMTI, simulated data are usually employed to support the training of networks, which has proved to be a feasible way in practice. Although some simulated data are very close to the real radar data, the fact is that distribution differences between them are inevitable and always lead to a performance loss of the network. Motivated by recent advances in transfer learning, this letter proposes a new method for ground moving target detection of multichannel SAR systems, namely, subdomain adaptive residual network (SARN). It is built on the basis of ResNet18, and subdomain adaptation is introduced. During the network training, multi-kernel local maximum mean discrepancy (MK-LMMD) is minimized as well as classification error. Experiments on three-channel SAR data show that the proposed method significantly improves the detection performance as compared with CA-CFAR and the DL method.

Ground armored weapons have a high detection value in military operations. Satellite synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level sizes limited by resolution of sensors. Airborne SAR have strict experimental conditions and cannot be applied in actual battlefield environments. MiniSAR sensors, which combine the advantages of submeter-level ultrahigh resolutions and flexible flight, play a crucial role in recognizing military targets. In this paper, various small military targets in real complex ground scenarios are detected with the MiniSAR of NUAA. However, there are still two difficulties. First, because of a limitation in the number of flight circles, the number of obtainable military target samples is not sufficient to adapt to the traditional deep learning methods that rely on a large number of image samples. Second, due to the imaging systems and different depression angle of MiniSAR, the SAR images of MiniSAR suffer from the same deformation challenge as the moving and stationary target acquisition and recognition (Mstar) with high depression angle. To address these two challenges, we propose a FASAR-Net framework based on few-shot learning with meta learning and adversarial domain learning, combined with the inherent scattering features of the SAR targets. Furthermore, we validate the reliability and accuracy of this algorithm on Mstar and our datasets, and the result of recognizing small SAR targets is compared with our algorithm and other classical algorithms. We conclude that the proposed algorithm has high accuracy in the recognition of the deformation small targets under the few sample condition.

Multi-input and multi-output (MIMO) radar is an advanced radar system, which grows into a promising candidate for the future synthetic aperture radar (SAR) because the MIMO radar can provide more degrees of freedom (DOFs). Monostatic MIMO SARs (where transceiver channels share the same antenna array) will produce lots of repeated equivalent phase centers (EPCs) due to the same wave path, and these repeated EPCs actually have no improvement to the DOFs of the radar system, resulting in a tremendous waste of the radar resources. To this end, this paper devises a novel radar framework, which is referred to as MIMO SAR with interpulse phase coding and multi-carrier (IPCMC). The IPCMC scheme employs multi-carrier and well-designed phase codes in transmitting channels, which has the advantage of increasing the DOFs in elevation. Concretely, by introducing the carrier information into the transmission-receiving spatial frequency domain, non-overlapped spatial frequency difference curves can be obtained to further increase the DOFs in elevation. Furthermore, an advanced range ambiguity separation method based on the proposed IPCMC MIMO SAR is presented. Compared with SAR systems with different numbers of DOFs, the proposed IPCMC MIMO scheme can precisely separate more ambiguous regions, due to the increased DOFs. Finally, detailed simulation experiments are carried out to verify the efficacy of the proposed IPCMC MIMO SAR.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this problem, we here propose an improved echo separation solution for realizing a low-cost MIMO-SAR system. We detail a generic waveform design scheme as well as optimized monostatic radar waveforms (e.g., nonlinear frequency modulation (NLFM) signal) showing how these can be directly adopted in the proposed scheme to improve the imaging performance. The proposed scheme enables the number of the interference segments generated by unmatched waveforms to be halved by the use of the fast time spectrum preprocessing and segment synthesis, dramatically simplifying the array configuration and reduces the system complexity. By exploiting inter-pulse phase coding techniques, the proposed method can provide a reconfigurable waveform transmitting scheme, allowing the system resources in range frequency, elevation space, and Doppler domains to be jointly exploited for the separation of aliased signal returns. The proposed scheme is evaluated using extensive numerical and measured data sets, demonstrating the feasibility and potential of the proposed method for resource-limited spaceborne/airborne MIMO-SAR systems.

In Video synthetic aperture radar (Video SAR) system, the moving target will leave a shadow at its actual position due to Doppler effect. As the shadow of the moving target moves between Video SAR frames, the amplitudes of pixel points at the corresponding positions will jump between frames as well. According to this characteristic, a new method of Video SAR ground moving target detection and tracking based on the inter-frame amplitude temporal curves is proposed in this paper. In this method, the specially designed multiple receptive field fusion neural network model based on frame variation (MRFN-FV) is used to classify the pixel points with obvious inter-frame amplitude jumps on the whole-time axis, and then the false alarms are suppressed based on the temporal change characteristics of pixel points. Finally, the improved clustering algorithm is used to detect, locate and track the moving targets in each frame of SAR images. The effectiveness of the proposed method is verified through the measured data recorded by the THz band Video SAR system.

In this article, a novel method for the moving target detection through multilevel spatial and channelwise attention network based on shallow feature channel module (MSCA-SFCM) is presented, and the circular spotlight (CSL) video synthetic aperture radar ground moving target indication (Video-SAR-GMTI) mode of the Nanjing University of Aeronautics and Astronautics miniature SAR (NUAA MiniSAR) system is introduced. However, due to the lack of moving target samples, MSCA-SFCM cannot be directly applied to the CSL Video-SAR-GMTI mode in the real system. To this end, this article proposes a training sample library construction scheme for moving targets of high verisimilitude. In this scheme, based on the radar system parameters, after the traversal of moving target parameters and SAR imaging, the scattering line characteristic of all possible moving targets under the current system parameters is simulated and then used for MSCA-SFCM network training. Afterward, the properly trained network can be used for moving target detection in real radar data. The effectiveness of the proposed method is verified by the NUAA MiniSAR system.

In recent years, convolutional neural networks (CNNs) have demonstrated significant potential for synthetic aperture radar (SAR) target recognition. SAR images possess a strong sense of granularity and contain texture features of varying scales, including speckle noise, dominant scatterers, and target contours, which are not typically considered in traditional CNN models. This article proposes two residual blocks, termed multibranch cross-channel attention (EMC2A) blocks, with multiscale receptive fields (RFs) based on a multibranch structure and designs an efficient isotopic architecture deep CNN (DCNN) called EMC2A-Net, whose structure is interpretable from a probability and mathematical statistics perspective. EMC2A blocks employ parallel dilated convolution with different dilation rates to effectively capture multiscale contextual features without significantly increasing the computational load. To further enhance the efficiency of multiscale feature fusion, this article presented a multiscale feature cross-channel attention module, known as the EMC2A module, which adopts a local multiscale feature interaction strategy without dimensionality reduction. This strategy adaptively adjusts the weights of each channel using efficient one-dimensional (1-D)-circular convolution and sigmoid function to guide attention at the global channel-wise level. Comparative results on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that EMC2A-Net outperforms the other available models of the same type and possesses a relatively lightweight network structure. The ablation experimental results further demonstrate that the EMC2A module significantly enhances the model’s performance by utilizing only a few parameters and appropriate cross-channel interactions.

The wavefront curvature error (WCE), which is caused by the planar wavefront assumption in the polar format algorithm (PFA), induces serious degradation to the formed image, especially for the bistatic synthetic aperture radar (BSAR). The existence of a double hyperbola in the bistatic range equation makes it difficult to derive the exact analytical expression for the WCE in the wavenumber domain. Actually, the distortion derives from the first-order WCE and the defocus mainly comes from the quadratic WCE. Moreover, the residual odd-order WCE also affect the main-lobe of the target and the residual even-order WCE lead to the problem of sidelobe asymmetry. To overcome the issues, in this paper, the first-order one-dimensional (1D) Taylor expansion is adopted to effortlessly separate the distortion error and the residual error. The proposed separation strategy can compensate the residual high-order error and maintain the image continuity. In addition, to avoid the interpolation error, which is induced by the wavenumber nonuniformity, the nonuniform fast Fourier transform (NUFFT) is adopted to construct the phase filter. The effectiveness of the proposed algorithm is demonstrated by simulated experiments.

The multirotor drone based miniature synthetic aperture radar (MiniSAR) has attracted attentions due to the low costs and flexible capacities. The circular trajectory enables it with the capability of 360° observation of the region of interest (ROI). However, the focusing ability of time-domain based imaging algorithms mainly depend on high precision inertial navigation system (INS), which is not available for the finite load of MiniSAR. In addition, in dealing with the problems of motion error compensation and sub-images registration, the present time-domain based noncoherent imaging algorithms rely on preset calibrators or isolated strong point-like scatterers in the observed scene, which restrict its practical applications. From the efficiency and practicability points of view, this paper presents a polar format algorithm (PFA) based noncoherent imaging strategy for multirotors borne circular MiniSAR. The effectiveness of the proposed method is verified by the real data experiments.

This paper deals with a novel transmitted scheme for a multi-subcarrier frequency MIMO-SAR system, which aims at suppressing the intrapulse repeater mainlobe-jamming and immensely improving the dynamic range of the receiver. To this end, due to the multi-subcarrier frequency transmission scheme, the mixed signal with true target signal and repeater jamming can be separated by a well-designed spatial-frequency filter. Thus, the range and direction of arrival (DOA) information is accurately estimated without interrupting the normal work of the radar. Furthermore, to further improve the orthogonality of transmitted waveform, the design of a phase-coded LFM waveform pair exhibiting both low cross-correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. Besides, to handle the resulting nondeterministic polynomial (NP) hard problem, an alternating direction multiplier method (ADMM) based optimization method is employed. Compared with the traditional jamming suppression method, the proposed method improves the degree of freedom (DOF) from the carrier frequency domain, and it has the capability to suppress the intrapulse repeater mainlobe-jamming. Finally, detailed simulation experiments are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

To improve the classification performance of the convolutional neural network (CNN) for polarimetric synthetic aperture radar (PolSAR) images with limited labeled samples, this letter proposes a memory CNN (MCNN) for semisupervised PolSAR image classification using both the labeled and unlabeled samples. Specifically, the MCNN introduces a memory module to realize an assimilation–accommodation interaction between the network and the module in the model training process. Compared with the traditional CNN-based methods, the advantage of the introduced interaction mechanism can exploit the memory information during the model training including both the learned feature representation and the model inference uncertainty. Under the framework of memory mechanism, the semisupervised learning can be implemented simply and effectively by introducing an unsupervised memory loss. We evaluate the proposed method on three benchmark PolSAR data sets. The experimental results show the advantages of the MCNN over the supervised, semisupervised, and unsupervised methods in the PolSAR image classification with limited labeled samples.

Wavefront curvature effects grow worse in spotlight Bistatic synthetic aperture radar (BSAR) imagery when reconstructed via polar format algorithm (PFA) under the large-scale scene. The wavefront curvature error, which causes geometric distortion and defocuses to the image, is induced by the faulty hypothesis of the planar wavefront. Due to the approximated or unsegmented wavefront curvature error, conventional compensation algorithms experience different degrees of performance restriction. In this letter, the geometric distortion error and the intact defocus error are separated and analyzed for the first time. Based on the separated phase error model, a modified space-variant post-filtering (MSVPF) algorithm is proposed to correct the wavefront curvature effects of the PFA image. Two major contributions of this algorithm are as follows. First, as no high-order term is ignored in the constructed space-variant filter, the proposed algorithm can compensate for the defocus error with any order. Second, MSVPF employs a separate strategy to correct the space-variant defocus and geometric distortion, which avoids high overlap rate in subimage processing and maintains the computational efficiency of the original SVPF. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.

Based on the principle of planar wavefront assumption, a novel point of view in processing the spotlight synthetic aperture radar (SAR) data is proposed in this letter. After match filtering and motion compensation to the scene center, from the perspective of range cell migration correction (RCMC), two scaling transforms (a range-frequency scaling with a subsequent azimuth-time scaling) are proposed to correct the range cell migration caused by the coupling between the range-frequency and azimuth-time. Moreover, to reduce the spectrum loss (the loss of image resolution), two constant scaling factors are introduced to optimize the range-frequency and azimuth-time scaling transforms, which enable the presented algorithm to realize the flexible and efficient imaging ability. Simulation results are conducted to validate the efficacy of this algorithm.

In recent years, convolutional neural networks (CNNs) have been successfully applied to inverse synthetic aperture radar (ISAR) sparse imaging because of their powerful ability in feature extraction. However, these CNNs only adopt single path feed-forward architectures and lack paths for directly transmitting original feature representations (OFRs) in shallow layers to reconstruction layers, which limits the complete reconstruction of target shape due to the underutilization of the OFRs that are efficient for recovering target details. Later, fully CNN (FCNN) introduces several skip connections (SKs) to establish the additional ways for directly passing the OFRs to the reconstruction layers. Nevertheless, the transmitted OFRs inevitably include the feature information of artifacts, which usually results the appearance of artifacts in final reconstructed target image. To address this issue, we introduce the gate units to FCNN, and refer to the improved FCNN as G-FCNN. Furthermore, the learnable gate units weight the OFRs transmitted by SKs and autonomously decide how many OFRs are transmitted further. To circumvent the shortage of the real data available for network training, we utilize the transfer learning strategy to guarantee a good performance of the G-FCNN. The imaging results of real data show that the G-FCNN-based imaging method is superior to the existing CNN-based imaging methods.

Ground moving target indication (GMTI), as a challenging task for synthetic aperture radar (SAR) systems, keeps drawing considerable attention. Robust principal component analysis (RPCA) aiming at separating low-rank and sparse components has been successfully employed in SAR systems for GMTI recently. However, its practical application is limited by the heavy computational burden as well as the requirement of manual parameter modification. To cope with this problem, a fast and free of presetting parameters RPCA network (RPCA-Net) is proposed for SAR-GMTI under strong clutter background. In the proposed method, a novel RPCA model is first introduced, where not only the low-rank and sparse terms but also the errors in practical SAR systems are taken into account. Moreover, the low-rank factorization plus scaled gradient descent (ScaledGD) is also employed to acquire low-rank clutter background rather than singular value decomposition (SVD). Then, we parameterize our proposed RPCA model and unfold it as a feedforward neural network (FNN) to acquire the iterative parameters through backpropagation. Compared to the GMTI methods based on traditional RPCA models, our proposed RPCA-Net can provide a higher detection ability and faster convergence without presetting parameters empirically. Experiments on two groups of measured data collected by airborne SAR systems validate the superior performance of the proposed RPCA-Net.

Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with$\ell _{1}$-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via$\ell _{2,1}$-norm is proposed. Compared with$\ell _{1}$-norm -based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.

An alternate transmitting mode (ATM) is an important synthetic aperture radar (SAR) imaging mode as it can provide rich waveform design degrees of freedom to improve the system performance, especially to mitigate range ambiguities. However, the azimuth ambiguity issue caused by the differences between the autocorrelation functions of transmitted waveforms is ignored in existing studies. In this article, a deep understanding of the ambiguities in the ATM allows a correct evaluation of the ambiguity-to-signal ratio and the design of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms optimized for ambiguity suppression. Moreover, a novel azimuth compensation method is developed to remove the azimuth ambiguities caused by waveform diversity. Finally, detailed simulation experiments are carried out to verify the theoretical analysis.

The range ambiguity issue is a technical challenge in the radar community and has been widely discussed over the years. Researchers have given special attention to multiple-input and multiple-output (MIMO) radar to address the range ambiguity because this radar system can employ more equivalent degrees of freedom. Open studies on MIMO radar are generally based on the assumption of orthogonal waveforms, whereas radar performance is seriously limited by distributed targets due to mismatched energy. To this end, this paper deals with a novel MIMO radar transmission scheme called transmitter interpulse phase coding (TIPC) without using orthogonal waveforms. First, a set of well-designed TIPC codes are employed to modulate transmitter subarrays with the same modulated signal. Second, in the case of high pulse repetition frequency (PRF)1, aliased echoes from different transmitted channels are directly separated by a group of simple Doppler filters; for normal PRF2radar systems, a technique called digital beamforming in azimuth is exploited to ensure an effective multiple waveform separation. Third, a decoding processing is performed for a further derivation of the residual TIPC matrix that is related with ambiguity order. Next, the desired and ambiguous echoes are separated by a specifically designed spatial filter that absorbs the residual TIPC matrix. Particularly, the separated signal can be used for some further applications such as increasing the observation swath. Finally, point-like target and distributed targets simulation experiments are performed to verify the feasibility of the proposed TIPC MIMO radar.

The echo separation issue for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is usually regarded as a more technical challenge. Spotlighted as a promising solution to the echo separation, the well-known short-term shift-orthogonal (STSO) beamforming scheme has become increasingly popular. However, for airborne MIMO SAR systems, the digital beamforming (DBF) involved in the STSO scheme usually encounters more issues, e.g., the direction of arrival (DOA) mismatch induced by topography variation. Up to now, relatively less research on robust DBF processing has been conducted for airborne MIMO SAR systems. In this respect, an adaptive DBF technique, based on interference plus noise covariance matrix (IPNCM) reconstruction and desired signal steering vector estimation, has been proposed in this paper. IPNCM reconstruction and steering vector estimation can not only cope with the DOA mismatch problem, but also remove the desired signal component in the training data cells to increase the beamformer convergence rates. Consequently, the proposed approach really improves the array output signal-to-interference-plus-noise ratio (SINR). Moreover, numerous discussions and simulations are carried out to prove the effectiveness of proposed DBF technique under various disturbance environments. Compared with the current DBF techniques, the proposed method provides a bright application prospect for the STSO scheme.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

To overcome the problem of coarse precision in traditional estimation algorithms for estimating the three-dimensional (3D) rotational vector, this paper proposes a novel approach to estimate the total rotational vector of noncooperative targets, which uses 3D Interferometric Inverse Synthetic Aperture Radar (InISAR) technique to obtain the position estimation and effective rotational vector of the targets. Additionally, the LV's Distribution (LVD) is applied to improve the estimate precision in solving the total rotational vector along the radar line-of-sight (LOS). Finally, the total rotational vector can be obtained by combing the aforementioned two procedures. The effectiveness of the three-dimensional imaging, and the accuracy of rotation vector estimation of the proposed method are demonstrated by simulation experiments.

Moving target brings out the different position shifts and defocusing across the image sequences acquired by circular synthetic aperture radar (CSAR) due to the Doppler shift and range smear effects. In this paper, a novel moving target detection approach for single-channel CSAR is proposed based on deep neural network (DNN). A dual-channel densely connected convolutional network (DenseNet) in consideration of complex-valued information is exploited for distinguishing the ground clutter and moving target. In terms of limited CSAR measure data set available for training the DNN network, simulated moving target samples are generated and fused into the measured ones under the various motion parameters. Finally, experiments have demonstrated that the proposed DenseNet for single-channel CSAR system processes an accepted detection performance and effectively overcomes the insufficiency of the limited dataset applications.

Video synthetic aperture radar (VideoSAR) possesses the capability of imaging and continuously monitoring the scenario from a wide-aspect interval for enhancing the performance of information interpretation. In this paper, we propose a complete imaging scheme to achieve the unified video coordinate system in high-resolution airborne VideoSAR configuration. Comprehensive postprocessing video imaging (PPVI) framework built on range Doppler algorithm and range migration algorithm is elaborated especially in terms of complex measured data, which is divided into three parts for ensuring the stability of video background: full-aperture imaging, 2-D autofocus technique, and Doppler spectrum segmentation. Experimental results utilizing the measured airborne data have demonstrated the effectiveness of PPVI scheme for sequential VideoSAR formation.

With the circular-scanning mode, we can obtain real-time synthetic aperture radar (SAR) images of wide areas including both sides of the nadir line, which considerably benefits the terrain matching procedure. However, due to the continuous rotation of the antenna beam, the azimuth Doppler bandwidth of the circular-scanning SAR data will increase noticeably, which in turn will lead to the azimuth spectrum aliasing phenomenon. Existing approaches for the azimuth processing of the circular-scanning SAR data are based on the subaperture technique. However, when the pulse repetition frequency (PRF) is limited, these approaches result in the loss of azimuth resolution. In this letter, a new circular-scanning approach based on the deramping operation is proposed. The deramping operation could solve the problem of spectrum aliasing and the loss of azimuth resolution can be avoided. The performance of the proposed imaging approach is demonstrated by point targets simulation and real circular-scanning SAR data processing results.

In this article, a new interpretation of the polar format algorithm (PFA) for general bistatic spotlight synthetic aperture radar (SAR) imaging is presented. From the viewpoint of 2-D decoupling, we examine the commonly adopted implementation of the PFA, i.e., the separable 1-D range and azimuth resampling procedures for their roles in range cell migration (RCM) correction, respectively. Utilizing this new formulation, we analyze the effect of range and azimuth resampling on the residual 2-D phase error and reveal the inherent structure characteristics of the residual 2-D phase error in the wavenumber domain. By exploiting the available a priori knowledge on the phase error structure, a structure-aided 2-D autofocus approach to refocus the defocused PFA imagery is proposed. The proposed approach fully exploits the potentiality of the available data and the a priori knowledge about the phase error that need to estimate, so the accuracy of the residual 2-D phase error estimation and correction can be greatly improved. Finally, experimental results are presented to show the effectiveness of the proposed approach.

The processing of synthetic aperture radar (SAR) echoes collected during radar antenna beam steering, such as in the staring spotlight, sliding spotlight, and TOPS operating modes, requires additional efforts due to the limited pulse repetition frequency (PRF). Subaperture processing is mostly used in these cases, but the complexity arising from subaperture dividing and recombination is preferably avoided. The two-step approach (TSA) is an elegant solution to full-aperture processing. Nevertheless, for high-resolution airborne and spaceborne SAR processing, the TSA is still faced with a number of difficulties, which does not occur, however, in subaperture processing. For instance, the high range bandwidth induces Doppler spectrum aliasing during the spectral analysis (SPECAN) stage of the original TSA. Moreover, the Doppler spectrum obtained in TSA, when inverse Fourier transformed back to the slow time domain, might also suffer from aliasing, which precludes the estimation and correction of the unknown residual motion error or tropospheric disturbance. In this article, we extend the TSA to address these problems and present a full-aperture processing framework to precisely focus the high-resolution airborne and spaceborne SAR data. Simulation of X-band spaceborne SAR echoes with transmission signal bandwidth of 3.6 GHz and coherent integration angle of 12.5° has validated the extended TSA (ETSA). Meanwhile, experimental X-band airborne SAR data with the same range and azimuth resolution are also processed using the ETSA, where the conventional autofocusing techniques have been smoothly incorporated.

Video synthetic aperture radar (VideoSAR) presents significant potential for improving the performance of information interpretation. Key frames represent the aspect-dependent electromagnetic energy, which frequently obscures other scattering physics dominated by specular returns. In this paper, we propose a vision-based background subtraction approach for capturing VideoSAR scattering key-frame information in simultaneously single-channel and single-pass configurations. The spatiotemporal key-frame extractor combines subaperture energy gradient with modified statistical and knowledge-based object tracker. It can robustly discriminate the scattering features of the alternation between transient persistence and disappearance. We evaluate the proposed method using several measured airborne data. Experimental results and performance comparison have demonstrated that the scattering key-frame extractor can achieve a high accuracy for VideoSAR summarization.

The traditional inverse synthetic aperture radar (ISAR) imaging uses the range-Doppler (RD) type of methods. The compressive sensing (CS)-based ISAR imaging is capable of obtaining good target images of high contrast and less sidelobe with much less downsampling data. However, the real application of CS ISAR imaging is limited by the time-consuming iteration-based image reconstruction. The image quality is also limited by the performance of sparse representation of the target scene. In recent years, deep learning methods, more specifically the convolutional neural network (CNN), has shown its capability in signal recovery with downsampling or noncomplete data. The well-trained CNN can extract high-level abstract feature representation from the input data autonomously and exploit it in the signal recovery. We are interested in exploiting the CNN to enhance the CS ISAR imaging capability. The successful training of CNN always requires many thousand annotated training samples. This limits the application of CNN to the radar imaging field where large amount of training data cannot be obtained as easy as in other fields, e.g., computer vision. We propose a fully CNN (FCNN) for ISAR imaging. The constructed FCNN has a multistage decomposition and multichannel filtering architecture and has no fully connected layers. It can work with very few training samples as compared to existing CNN-based imaging networks. The imaging results of real ISAR data show that the proposed FCNN-based ISAR imaging method outperforms the state-of-the-art CS ISAR imaging methods in both image quality and computational efficiency.

Video synthetic aperture radar (VideoSAR) provides a continuously multidimensional phase-history acquisition, proved to be significantly attractive in the remote sensing-based information extraction field. In this paper, we propose a 3-D structure-from-motion (SfM) retrieval method based on the cylinder model from circular VideoSAR sequences. First, VideoSAR imaging characteristics on the cylinder model are analyzed. Then, SfM retrieval method with geometric prior knowledge is proposed using robust shadow information. Finally, experimental results utilizing circular VideoSAR fragment published by the Sandia National Laboratories indicate that the proposed method can achieve a higher accuracy, hence, the validity has been demonstrated.

Land cover classification is an important part of the polarimetric synthetic aperture radar (PolSAR) image interpretation. The convolutional neural network (CNN) has been utilized to improve the classification accuracy recently. However, how to efficiently train the classification model with limited training samples while keeping the generalization performance is still a challenge. In this letter, we devise a subspace learning network (SSLNet) for PolSAR image classification, which can be trained more efficiently. First, a third-order polarimetric feature tensor is constructed using five-target decompositions to make full use of the prior knowledge. The tensor is then fed into a two-layer CNN in which the principal component analysis (PCA) is employed to learn the convolutional filters. Finally, the output features of the network are obtained by binary hashing and block-wise histograms, followed by the nearest neighbor (NN) classifier to complete the classification. Due to the simple learning strategy, the proposed SSLNet can be easily designed and efficiently trained. Experimental results on benchmark PolSAR data reveal that the SSLNet can achieve higher classification accuracy with limited training samples than the conventional CNN method.

To satisfy the demand of SAR real time imaging and improve processing efficiency of the polar format algorithm. In this paper, PFA is implemented by twice SINC interpolation, and SINC interpolation is realized by parallel structure block RAM group. First, the floating point sampling coordinates are converted to the fixed point, and the raw data and coordinate's fractional part are written into the block RAM group with coordinate's integer as address. Finally the raw data is weighted and summed to gain the interpolation result in a clock. The parallel structure is simple and easy to implement, pipelined output the result, interpolation points can be down compatible, and resource utilization will not increase. The system is built on the VC690T, it can process 8k by 8k complex-image single-precision floating-point within 0.79s, when the system works at 200MHz.

With the development of spaceborne synthetic aperture radar (SAR), the high resolution has become a hot topic again in recent years. However, the higher the resolution is, the worse the accuracy of the traditional hyperbolic range model (HRM) gets. In the case of ultra-high resolution spaceborne SAR, the curved orbit should be taken into account. To overcome this problem, we present a modified data-focusing scheme integrated with state-of-the-art airborne SAR motion compensation methods for spaceborne spotlight SAR. Firstly, one-step motion compensation is selected to correct the orbit curvature effectively, which has been applied in ultra-high resolution airborne SAR systems. Meanwhile, the two-step approach (TSA) is introduced to solve the azimuth spectral folding phenomenon. The simulation results validate the effectiveness of the scheme.

Due to the limitations of large-scale APD arrays, the traditional airborne LiDAR systems can hardly achieve significant improvement of 3D imaging resolution. To overcome this difficulty, a novel airborne array-encoding LiDAR is proposed in this paper. The system structure and work principle are first presented. Then the key modules involving encoding, multiplexing and data decoding are introduced. In particular, the data decoding method is designed and verified for data processing of the encoded full waveforms. The experimental results indicate that the proposed LiDAR can complete 128×128 -pixel 3D imaging with only 64-element APD array under scannerless condition by employing 16×16 array-encoding.

When processing high-resolution spaceborne synthetic aperture radar (SAR) data, the orbit curvature is a key aspect that must be taken into account. The non-hyperbolic range history makes most SAR imaging approaches not suitable for the curved orbit. Based on the two-dimensional spectrum derived by series reversion (SR), a modified Omega-K algorithm (OKA) is proposed in this paper. Making use of the reference function calculated by SR, an accurate bulk compression is implemented. Following, a modified Stolt interpolation is applied based on least square (LS), to perform the residual range-variant processing efficiently. The method described can achieve satisfactory focusing results for spaceborne SAR, without a large number of computation. Point targets simulations have validated the presented research.

Airborne synthetic aperture radar (SAR) image quality considerably degrades because of motion errors. High-precision motion compensation (MOCO) is necessary in an advanced SAR data processing scheme. Operation complexity and computation burden are both increased as development of ultra-high resolution SAR. There are two main disadvantages for conventional MOCO. Firstly, accurate envelope correction should be performed by complicated interpolation, expending a large number of computing resources. In addition, interpolation makes a separate process, which could not be integrated into imaging algorithms easily. Secondly, since the fixed processing flow of conventional MOCO, phase correction is seriously influenced by the accuracy of envelope correction. An efficient MOCO approach is presented in this paper. A novel calculation formula of line-of-sight (LOS) range displacement is introduced from another perspective. On this basis, modified phase correction turns to be performed before envelope correction, on the premise that the accuracy of signal phase should be guaranteed. Consequently, an approximate envelope correction without interpolation can be adopted using subswath, to improve the processing efficiency. Simulations with point targets and processing of real data are used to confirm the validity of the proposed approach.

Sliding spotlight synthetic aperture radar (SAR) is a rising imaging mode, whose azimuth resolution is higher and imaged area is larger. When processing data, two key problems should be considered. Firstly, system's pulse repetition frequency (PRF) is always insufficient, which introduces aliasing into the azimuth spectrum. Secondly, the effect of motion error enhances because of longer synthetic aperture, consequently the accuracy of motion compensation (MOCO) should be increased. This paper presents a modified imaging scheme based on subaperture. Subaperture method is used to overcome the problem that PRF is insufficient. Meanwhile, processing of subaperture data chooses high precision line-of-sight (LOS) motion compensation, improving focused quality. The presented algorithm can attain 0.1m azimuth resolution and has the value of practice. Point targets simulation and processing of real data are used to confirm the validity of the proposed approach.

The Doppler shift effect results in some targets shadows in theirs actual position, and a strong correlation exists between adjacent frames of Video Synthetic Aperture Radar (VideoSAR) imagery. Based on the above rationale, a novel approach to moving targets shadow detection for high-frame-rate VideoSAR imagery sequence is presented. First, a fast preprocessing stage is essential in real applications, where the SIFT with RANSAC registration algorithm is employed to compensate for the changing background, and the CattePM model is used to suppress the speckle noise. Then, in order to separate the targets and the background automatically, a threshold segmentation algorithm, called maximizing the Tsallis entropy, is applied. Finally, background difference with three frame difference method implements the precise moving targets extraction. Experimental results utilizing VideoSAR imaging fragment show that multiple moving vehicles are detected effectively and, hence, the validity has been demonstrated.

Due to a long coherent processing interval, moving targets are severely smeared in the UHF-band synthetic aperture radar (SAR) imagery. This further results in a low signal-to-clutter-and-noise ratio, which might lead to an unacceptable false-alarm rate in multichannel ground moving target indication. A method of moving target screening is presented in this letter, which serves to determine whether the target detected by a constant false-alarm rate detector is a real moving target. An inverse omega-K algorithm is implemented, which can recover the Doppler phase history of any isolated target within a clutter-suppressed omega-K SAR image. The recovered data are again processed into a subimage by a simple range-Doppler algorithm. Then, the subimage is refocused by azimuth autofocus processing. The sharpness of the subimage will not change after refocusing if it only contains stationary targets; otherwise, the sharpness will significantly improve. We can eliminate a false moving target by detecting this change. The proposed method is demonstrated on simulated and real multichannel UHF-band SAR data.

An efficient approach to achieve ground moving target indication (GMTI) for synthetic aperture radar (SAR) is to use the Monopulse-SAR system. This paper examines the statistics of monopulse ratio (MR) for SAR/GMTI model when complex Gaussian clutter-plus-noise is considered. The probability density function (pdf) of MR is analyzed in detail. Especially, the conditional likelihood function of MR under the null hypothesis is given in a closed-form defined by special functions. An automatic constant false-alarm rate (CFAR) detector for moving targets is provided and extended to a multi-MRD form to further improve the final detection performance. Experimental results are presented to examine the detection performance and validate the theoretical analysis.

SAR (Synthetic Aperture Radar) imaging processing is of high computations and its imaging algorithms are often complicated. How to implement SAR imaging efficiently and in real time is a deserving research. Autofocusing is an essential chain of SAR processing and is also computation-consuming. We know that FPGA (Field Programmable Gate Array) has many advantages such as being of large scale, high-performance and reconfigurable and so on. So we would like to utilize FPGA to implement SAR autofocus algorithms in real time. In this paper, implementations of two popular autofocus algorithms of SAR imaging, namely, PGA (Phase Gradient Autofocus) and PAST (Projection Approximation Subspace Tracking) are discussed in detail based on KintexTM-7 FPGA of Xilinx Corporation. State machines and hardware mappings of each are designed respectively with pipelined processing and floating IP core which ensures all the necessary computations of floating numbers efficiently. Our experimental results show that it takes both algorithms about 0.5 seconds to completely process a defocused SAR image of 2k*2k 64-bit samples with system clock 200MHz, which authenticates the valid and credible real-time implementations. This work lays the foundation of the FPGA combination of autofocus algorithms with basic imaging algorithms to construct a complete real-time SAR processing system in the future.

To satisfy the demand of real-time processing of airborne synthetic aperture radar (SAR), the polar format algorithm (PFA) is designed and implemented based on FPGA. In this design, the complicated two-dimensional interpolation of the canonic PFA is implemented using the principle of chirp-scaling (PCS) to improve the efficiency, the Floating-Point IP core and pipeline structure is applied to the realize high-speed floating-point-number computation, and an efficient scheme employed to control the read and write mode of DDR3 SDRAM is brought up to realize the transposition of matrix data demanded by the algorithm. The system is built on the KC705 evaluation board, and in a test with real SAR data, it takes approximately is to process 4096*4096 single-precision floating-point pixels with reasonable imaging result, when the system works at 200MHz.

An improved space-variant postfiltering approach to compensate for the wavefront curvature effect in polar format imagery is presented in this letter. The main contribution of this new method is the construction of a space-variant filter, which is based on exploiting the endomorphism property of the polar format transformation. The new approach provides an accurate and general solution to wavefront curvature compensation under arbitrary radar flight path. Finally, point target simulation has validated the effectiveness of the new approach.

The image formation scheme for circular-scanning synthetic aperture radar includes generating a set of focused subimages and the followed mosaiking processing. However, due to the particular acquisition geometry and irregular motion of the radar platform, the inevitable geometric distortions in the subimages are necessary to be corrected. In this letter, a 2-D geometric-distortion correction algorithm based on projection transformation between the scatterers and the images is presented, in condition of focusing the subimages using a linear range-Doppler algorithm. The point target simulation and real circular-scanning data implementation results are provided to demonstrate the validity of the proposed method.

A new approach to process stepped chirps in spotlight synthetic aperture radar is presented in this letter, which is based on exploiting the principle of chirp scaling (PCS). In particular, PCS is integrated into the polar format algorithm (PFA), obtaining a more efficient solution compared with the existing polar interpolation technique. The main contribution of this letter is the implementation of azimuth scaling, in which the bandwidth synthesis is embedded. The algorithm is developed dedicatedly for dealing with stepped chirps. The signal processing flow is investigated in detail, in which no interpolations but only fast Fourier transform and complex multiplications are involved. Point-target simulation has validated the new approach and indicated that it is more efficient than the classic interpolation-based one. The achieved computational gain measured in execution time is around 25%-30%.

In this letter, a novel global approach to range alignment for inverse synthetic aperture radar (ISAR) image formation is presented. The algorithm is based on the minimization of the entropy of the average range profile (ARP), and the processing chain is capable of exploiting the efficiency of the fast Fourier transform. With respect to the existing global methods, the new one requires no exhaustive search operation and eliminates the necessity of the parametric model for the relative offset among the range profiles. The derivation of the algorithm indicates that the presented methodology is essentially an iterative solution to a set of simultaneous equations, and its robustness is also ensured by the iterative structure. Some alternative criteria, such as the maximum contrast of the ARP, can be introduced into the algorithm with a minor change in the entropy-based method. The convergence and robustness of the presented algorithm have been validated by experimental ISAR data.

In this work, a 2-D subaperture polar format algorithm (PFA) based on stepped-chirp signal is proposed. Instead of traditional pulse synthesis preprocessing, the presented method integrates the pulse synthesis process into the range subaperture processing. Meanwhile, due to the multi-resolution property of subaperture processing, this algorithm is able to compensate the space-variant phase error caused by the radar motion during the period of a pulse cluster. Point target simulation has validated the presented algorithm.

The images generated by a circular-scanning synthetic aperture radar (SAR) can provide precise guiding information though image-matching post-processing, which necessitates their high precision in geometry. However, due to the irregular motion of the radar platform and the circular scanning antenna beam, the inevitable geometric distortion in the focused images is necessarily to be corrected. In this paper, a two-dimensional geometric distortion correction algorithm based on projection transformation between the scatterers and the images is presented, in condition of focusing the subimages using linear range-Doppler algorithm. The geometric distortion in any subimage obtained at any squint angle within 360 degrees can be effectively corrected. The point-target simulation results are provided to demonstrate the validity of the proposed method.

The frequency scaling algorithm (FSA) was proposed to process the synthetic aperture radar (SAR) data acquired via the dechirp-on-receive approach. Some aspects of improving the FSA are investigated in this paper, based on which an extended FSA (EFSA) is presented. The general purpose of the EFSA is to reduce the effect of range spectrum shift of the intermediate processing results, which occurs during the scaling operation in the FSA, so as to achieve a more effective utilization of the processed bandwidth. The EFSA is implemented through time shifting the scaling and the inverse scaling functions used in the FSA and also the adjustment of the scaling factor. The derivation of the EFSA is detailed in this paper. Point target simulation in squinted imaging geometry indicates that the presented algorithm is more suitable for large-squint applications.

Synthetic aperture radar (SAR) image formation for a ground moving target necessitates the compensation of the unknown target trajectory. The keystone transform has been employed to remove the linear component of the range migration for the moving target, where interpolation is required. In this letter, a realization of the keystone transform avoiding interpolation is presented. The kernel of this transform, i.e., the range-frequency-dependent azimuth time rescaling, is implemented using only complex multiplications and fast Fourier transforms based on the scaling principle, which has been successfully applied in the equalization of the space-variant range cell migration in SAR processing. In addition, the moving target is coarsely focused according to the SAR geometry and the platform velocity while exploiting the scaling principle. This preliminary focusing is helpful in the isolation of the moving target from ground clutter, so as to facilitate a more refined processing with respect to each mover. SAR raw data combined with simulated echoes of moving targets are utilized to validate the presented approach

Basic subaperture processing for synthetic aperture radar (SAR) imaging consists of generating a set of low-resolution images and adding them coherently to form the final high resolution image. For an airborne high squint imaging mode SAR, the coherent addition processing suffers from the geometric distortion in the subaperture images caused by range-Doppler interaction and the irregularities in aircraft motion. This paper presents a method to compensate these effects based on the geographical coordinate transform, which is used as a middle processing prior to the coherent summation of the subaperture images. It mainly involves coordinates calculations between the focus target plane and the image display plane under the flat-Earth model, and then a 2D interpolation is carried out in the image domain. A quantitative evaluation using point-target simulations of the coherent subaperture imaging algorithm for a squint single of 60deg is also provided. Its performance successfully demonstrates the validity of the proposed method. The further advantage of implementing this approach is that the crucial step of compensating the spatially-variant residual phase error can be done at the same time, and the resultant images with constant sample spacing are ready to be mosaiced to produce the full-strip image

Inverse Synthetic Aperture Radar (ISAR) is used to image noncooperative moving targets such as aircrafts, ships and celestial objects. The target rotation relative to the radar is the source for obtaining cross-range resolution in ISAR imaging. In airborne ISAR imaging of ships, the composition of the relative motion is more complicated than in other cases. One component is produced by the relative movement between the radar and the target. The other comes from the ship sway (roll, pitch and yaw). Furthermore, the practical sea-state changes frequently, and is also unpredictable. All of these increase the difficulty in image formation. In This work, the airborne ISAR imaging of ship targets is substantially discussed, and we design a simulation software kit applicable to practical sea-state and arbitrary flight path. By using the simulated data, the effects of the various relative rotations between the radar and the target on the ISAR image are clearly demonstrated, and this simulation work provides good experiences for further study. Finally, some imaging results under certain simulation conditions are presented.

The operation and processing for an airborne scan mode patch-mapping synthetic aperture radar (SAR) system are discussed, which serves for moderate fine-resolution mapping of medium-sized terrain patches. The scanning angular scope and velocity are determined by the desired imagery patch size and cross-range resolution respectively. The linear range-Doppler algorithm is employed in image formation. Finally, the experimental result of the test campaign of the described system is also presented in the paper.