It is challenging to achieve bistatic inverse synthetic aperture radar (Bi-ISAR) imaging and scaling for maneuvering targets. In the Bi-ISAR system, high-order translational and spatial variant (SV) rotational motion errors induced by the target’s maneuvering characteristics and time-varying bistatic angle would severely blur the imaging result. Moreover, both range and cross-range scaling (2-D scaling) are needed to exploit the size information of the target in practical applications. By parametric global modeling and extracting the coupling relationship between the target’s rotational motion and time-varying bistatic angle, this article presents a new Bi-ISAR imaging framework to achieve the integration of high-order motion compensation and 2-D scaling (IHOMC-2S) for maneuvering targets. First, a multidimensional motion errors signal model is developed. Based on the established parametric global model, a joint high-order translational motion compensation and SV autofocus method (JHTSVA) is presented via parametric minimum entropy optimization with the quasi-Newton solver. Then, with the estimated optimal parameters, the effective rotational velocity (ERV) and distortion coefficient can be estimated simultaneously by solving a 1-D unconstrained optimization problem. In addition, in order to successfully perform the 2-D scaling, a data-driven initial bistatic angle estimation method based on the linked feature scatterers is given. It is worth noting that the linear geometric distortion must be corrected before 2-D scaling, otherwise the sheared Bi-ISAR image may lead to an unreliable target recognition result. Finally, underpinned by the efficient and robust approach, IHOMC-2S can achieve high-resolution Bi-ISAR imaging and scaling for maneuvering targets avoiding the selection of prominent scatterers. Several experiments confirm the feasibility and robustness of the proposed algorithm.

Single-channel spaceborne-maneuvering bistatic forward-looking synthetic aperture radar (SS-BFSAR) enables the maneuvering platform to achieve high-resolution forward-looking imaging without deploying additional antennas or transmitting the radar signal. Nevertheless, achieving the suppression of spatial variant clutter with only the single-channel configuration remains a critical challenge for the ground moving target imaging (GMTIm) mission of SS-BFSAR. This study proposes an enhanced clutter suppression and GMTIm algorithm for SS-BFSAR. First, the proposed modified deramp-keystone processing (DKP) completely decouples the echo signal in range and azimuth while avoiding the signal-to-clutter ratio (SCR) degradation caused by the azimuth spectrum aliasing. Subsequently, two pre-focusing approaches are developed with nonlinear chirp scaling (NCS), i.e., global NCS and block NCS, to achieve deep focusing of spatial variant clutter while introducing differences of Doppler frequency position (DFP) between the clutter and GMT. These approaches preserve the clutter consistency of the pre-focus results, thereby ensuring that the GMT signal exhibits high SCR following the image domain cancellation. Finally, a matched filter and the proposed monostatic-equivalent model are used to refocus the GMT and estimate its velocity. The proposed algorithm can simultaneously obtain the well-focused ground scene image and the GMTIm result without DFP drift caused by the target’s motion. Comparative experiments using simulation and real data demonstrate the effectiveness and superiority of the proposed algorithm.

Hyperspectral remote sensing images (HSIs) capture detailed spectral characteristics of features, while multispectral remote sensing images (MSIs) provide clear spatial distribution. Fusing these two types of images can enhance feature identification and classification accuracy. Current deep learning algorithms achieve high fusion quality but struggle with balancing global effective perception and lightweight computation. Moreover, these algorithms typically discretely handle data mapping, which contrasts with the continuous nature of the world. Recently, the Mamba has shown significant potential for complex long-range modeling, addressing the computational complexity of global perception. Concurrently, implicit neural representation (INR) offers high-quality solutions for continuous domain modeling. To this end, this study introduces a novel network architecture that combines Mamba and INR, termed the Mamba cooperative INR fusion network (MCIFNet). MCIFNet effectively captures global image information and generates fused images in a continuous domain through point-to-point processing. The network comprises two main units: potential space projection and semantic extraction and fusion. The potential space projection unit performs shallow encoding of hyperspectral and MSIs, mapping them to a latent feature space. The semantic extraction and fusion unit (SEFU) uses scale adaptive residual state spatial and implicit spatial-spectral fusion (ISSF) modules to extract deep features from the bimodal images, generating fused images point-by-point. A series of fusion experiments with$4\times $,$8\times $, and$16\times $scale factors demonstrate that MCIFNet surpasses popular algorithms in both spatial detail and spectral information reconstruction, while also providing more lightweight performance. The code for MCIFNet will be shared onhttps://github.com/chunyuzhu/MCIFNet.

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness.

This letter proposes a method of jointly designing orthogonal frequency division multiplexing (OFDM)-LFM waveforms and receive filter in spatial heterogeneous clutter. Different from the previous indirect method that first solves the optimal spectra and then optimizes the waveform parameters to approximate the spectra, this letter proposes a cyclic algorithm based on iterative sequence optimization (ISO), which can directly optimize multiple groups of subchirp durations of the OFDM-LFM waveforms, so as to improve the output SCNR. Finally, numerical results are provided to assess the proposed method. Results indicate that the waveforms optimized by the proposed method can simultaneously suppress clutter in both spatial azimuth and frequency domains. Moreover, compared with the indirect optimization method, the method that only optimizes the transmitting waveforms and the single-group subchirp durations optimization method, the proposed method has higher output SCNR.

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. Experimental results show that compared with conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in SAR imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.

When the locations of non-zero samples are known, the Moore-Penrose inverse (MPI) can be used for the data recovery of compressive sensing (CS). First, the prior from the locations is used to shrink the measurement matrix in CS. Then the data can be recovered by using MPI with such shrinking matrix. We can also prove that the results of data recovery from the original CS and our MPI-based method are the same mathematically. Based on such finding, a novel sidelobe-reduction method for synthetic aperture radar (SAR) and Polarimetric SAR (POLSAR) images is studied. The aim of sidelobe reduction is to recover the samples within the mainlobes and suppress the ones within the sidelobes. In our study, prior from spatial variant apodization (SVA) is used to determine the locations of the mainlobes and the sidelobes, respectively. With CS, the mainlobe area can be well recovered. Samples within the sidelobe areas are also recovered using background fusion. Our method is suitable for acquired data with large sizes. The performance of the proposed algorithm is evaluated with acquired space-borne SAR and air-borne POLSAR data. In our experiments, we use the [Formula: see text] space-borne SAR data with the size of 10000 (samples) × 10000 (samples) and [Formula: see text] POLSAR data with the size of 10000 (samples) × 26000 (samples) for sidelobe suppression. Furthermore, We also verified that, our method does not affect the polarization signatures. The effectiveness for the sidelobe suppression is qualitatively examined, and results were satisfactory.

Accurately recognizing the type of complex electromagnetic jamming is the essential prerequisite for synthetic aperture radar (SAR) anti-jamming. However, current convolutional neural network (CNN)-based SAR jamming recognition methods require balanced training samples, which contradicts the varying difficulty of acquiring various jamming types, drastically reducing the recognition accuracy and generalization ability. This article proposes a discriminative feature distance metric model, JRSNet, for jamming recognition under imbalanced training samples, by refining the jamming modulation differences in the time-frequency (TF) domain into discriminative features. Novel feature discriminative distance metric (FD2M) loss function and discriminative feature constraint module (DFCM) are put forward to guarantee JRSNet learns embedding expression paradigm from jamming TF spectrograms to discriminative features, thus eliminating the influence of imbalanced training samples. Moreover, new spatial and channel attention modules are incorporated into JRSNet to capture jamming modulation information from multiple dimensions, consequently further improving recognition accuracy. Precisely because of the captured modulation regions in feature maps by spatial attention, the proposed approach can achieve jamming suppression synchronously. Experimental results show that under imbalanced training samples, JRSNet can accurately identify multiple jamming types both within and outside the training dataset with high generalizability. Compared with the existing jamming recognition methods, JRSNet performs superior recognition while taking into account good jamming suppression performance.

The imaging quality and efficiency are equally important in inverse synthetic aperture radar (ISAR) imaging. The high-order spatial variant (SV) phase errors induced by the target’s nonuniform rotational motion would seriously defocus the ISAR imaging results. The focused image can be obtained by exhaustive parameters estimation or optimization processing. However, the high-computational complexity limits its application in real-time imaging. To overcome this constraint, we propose an efficient ISAR imaging and scaling method for highly maneuvering targets by the integration of integrated cubic phase function (ICPF) and parametric spatial variant autofocus (PSVA) in this article. A novel rotational motion parameter estimation method based on ICPF, which only utilizes second-order phase term coefficients, is presented. Then, a parametric global model is established, which can achieve spatial variant (SV) autofocusing of defocused images based on estimated rotational motion parameters. Meanwhile, the cross-range scaling can also be realized using estimated effective rotational velocity (ERV). Without exhaustive parameters estimation and optimization search, the proposed ICPF-PSVA method not only achieves high-precision ISAR imaging but is also computationally efficient compared with the existing methods. Experiments using simulation data and measured data confirm the high efficiency of the proposed method in generating focused images of maneuvering targets.

Appropriate modeling and processing of echoes are the foundations for high-precision frequency-domain synthetic aperture radar (SAR) imaging. The complex geometry makes it challenging to accurately characterize and cope with the 2-D spatial variation of Doppler modulation in maneuvering-platform translational-variant bistatic forward-looking SAR (MTV-BFSAR), resulting in that the azimuth processing method by means of setting reference points on the Cartesian coordinate axis significantly impairs the performance in terms of robustness, accuracy, and efficiency. This article proposes a comprehensive MTV-BFSAR imaging algorithm based on universal frequency-domain azimuth signal modeling (UFDASM). The presented methodology utilizes the bistatic bisector to form the azimuth reference line (ARL) and develops two expeditious ARL and range isoline (RIL) coordinates’ solving techniques, which substantially augments the reliability and efficiency of the space-variant Doppler modulation coefficient (DMC) representation, reduces the order, and improves the robustness of the entire algorithm. Subsequently, thanks to UFDASM, a modified nonlinear chirp scaling (NLCS) method with orthogonal impulse response function (IRF) is derived to eliminate the spatial variation of the quadratic DMC in the range-Doppler domain while omitting that of the cubic one. Furthermore, one may find that the equalization of the second-order spatial variation introduces a cubic phase error (CPE) term. However, boundary analyses manifest that this error is small enough not to affect the imaging performance in MTV-BFSAR. Finally, the accuracy, robustness, and efficiency of the approach are validated through numerical simulation and raw data processing.

As an active radar system, synthetic aperture radar (SAR) is often affected by different types of strong, complex, and variable electromagnetic interferences, which severely degrades the final imaging performance. Thus, how to effectively detect and suppress complex electromagnetic interferences is a crucial challenge currently. In this paper, we propose a self-supervised learning interference suppression method based on deep learning, including interference localization filtering and radar signal recovery. First, we construct a novel convolutional Autoencoder deep learning model —LocNet via the proposed optimization criterion, which is utilized to detect and locate the interference for subsequent filtration. Aiming at the issue of signal loss in the filtering process that is generally ignored in the current literature, we then reconstruct a novel U-Net neural network model—RecNet for the low-loss recovery of signal. Compared with the traditional parametric/non-parametric anti-interference methods, the most significant advantage of our method is that it overcomes the requirement for interference priori information, which is more consistent with the actual situation, and effectively solves the target information loss. Furthermore, since no interference information is involved in the training process (self-supervised training), our method applies to multiple types of interference rather than a specific one. Moreover, with our method, interference detection and suppression can be achieved simultaneously instead of separating the two steps as in existing literature. Measured and simulated SAR interference-contaminated data test results validate the effectiveness and robustness of the proposed method.

Traditionally, when multiple targets appear within the radar beam at the same time, the range profiles of different targets are coupled together, the existing algorithms usually image each target separately due to the different motion states of the targets, making it impossible to image multiple targets simultaneously. To overcome this problem, this paper proposes a joint translational motion compensation and imaging method for multiple targets based on an integrated Kalman filter (IKF), which can realize the integration of tracking and imaging for multiple targets. Firstly, an integrated Kalman filter for wideband radar tracking is employed to predict as well as accurately estimate the next-moment motion state of multiple targets simultaneously. Then, with the precisely estimated motion state of the next moment, a joint translational compensation method with a blocked Fourier compensation matrix (BFCM) is proposed in order to compensate for the translational motion of multiple targets simultaneously, which uses the characteristics of the multi-target’s echo signal separated in the range time domain. Finally, by using the IKF and BFCM, the sequential translational motion compensation for multiple targets can be achieved, and the well-focused ISAR images for multi-target are obtained. Finally, the effectiveness of the method is verified by simulated and real data.

Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is of the capability to achieve unambiguous 2-D images in the forward-looking slight direction. FLMC-SAR imagery usually suffers from relatively low spatial resolution as only limited Doppler diversity can be generated from the synthetic aperture. In this article, a sparsity-driven resolution enhancement algorithm is proposed to improve the resolution FLMC-SAR image of the forward-looking area. Different from conventional beamforming processing to resolve the FLMC-SAR left–right ambiguity, a Bayesian sparsity reconstruction optimization is developed for jointly ambiguity resolving and resolution enhancement in the azimuth angle image domain. The spatial structure of the target in the preliminary image domain is used as the signal sparsity with prior information to solve the constrained optimization problem for FLMC-SAR image resolution enhancement. A local least square estimator of the prior noise and signal statistics in the FLMC-SAR nonisotropic image is established in terms of determining the sparsity weight parameter. Extensive simulation and real FLMC-SAR data experiments confirm that the proposed algorithm is capable of achieving the unambiguous and resolution-enhanced FLMC-SAR image.

The interrupted sampling repeater jamming (ISRJ) can effectively degrade the image quality and affect the subsequent target recognition by creating deceptive multiple false targets on synthetic aperture radar (SAR) images. A time-domain filtering method based on pulse coding to counter ISRJ is proposed in this article. First, this coding method requires the radar to transmit a full pulse signal consisting of multiple subpulse signals several times in the original pulse repetition interval (PRI), and there is a difference in the time distribution of the subpulses transmitted at different moments. Then, use the observation matrix determined by the echo conditions contained in each receiving window to filter the echo in the time domain to obtain the echo corresponding to each subpulse. Finally, the subpulse echo of the jammer sampling section is discarded and the remaining uninterfered subpulse echoes are segmented for pulse compression and subsequent imaging processing to obtain SAR images with a low jamming-to-signal ratio (JSR). Several groups of simulations show that the proposed method is effective against different kinds of ISRJ, and this time-domain filtering method can improve the effect of radar anti-ISRJ and has a high freedom of waveform design.

Accurate range model with acceleration, the coupling phase terms, and spatial-variant (SV) Doppler parameters are the main issues to be solved in high-speed-high-squint SAR (HSHS-SAR) with a curved trajectory. For these issues, an extended Omega-K (EOK) algorithm is developed in this paper. The proposed EOK algorithm mainly includes the following four aspects. Firstly, a modified range model (AMRM) considering three-dimension acceleration for a curved trajectory is established. Then, the coupling between the range and azimuth direction is removed by the modified Stolt mapping (MSM). Subsequently, an improved high-order spatial-variant (SV) phase correction approach is derived to eliminate the azimuth dependence of Doppler parameters. Finally, in order to avoid zeros-padding operation, the proposed method focuses on the sub-aperture data in the range time and azimuth frequency domain through data aligning processing. The experimental results of both simulation and real data verify the effectiveness of the proposed method.

Airborne forward-looking radar (AFLR) imaging has attracted a lot of attention in fields of Earth observation, independent of weather and daytime. However, the forward-looking imaging quality is not very high. To solve this problem, a sparse superresolution imaging algorithm for AFLR with multiple frames space is proposed. Firstly, the echo model for AFLR is introduced. Then, a multiple frame space is constructed to better describe the distribution of noise and targets of the imaging scene. Finally, the forward-looking imaging problem is constructed as the optimization problem, and the Bayesian framework is used to perform the superresolution imaging. Simulation results and real data results are given to verify its effectiveness.

Highly squinted synthetic aperture radar (HS-SAR) data focusing is a challenging task due to the heavy coupling between the range and azimuth, which would lead to the failure of the traditional imaging algorithms. In order to overcome these issues, a modified nonlinear chirp scaling (CS) algorithm for HS-SAR is proposed in this manuscript. First, traditional linear range walk correction (LRWC), range compressing (RC), secondary range compressing (SRC), range curvature correction (RCC) are employed as the range processing. Subsequently, a modulation phase factor (MPF) is introduced to weaken the influence of spatial variant (SV). After that, a high order SV correction phase (SVCP) approach is derived to eliminate the azimuth dependence of Doppler parameters. In addition, the sub-aperture data is focused on the range time and azimuth frequency domain by SPECAN processing to avoid numerous zeros-padding operations. Real data processing is adopted to prove the efficiency and validity of the proposed method.

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

Current subaperture-based azimuth-variant motion compensation algorithms for synthetic aperture radar (SAR) imagery usually suffer from the challenge of keeping high precision and efficiency simultaneously. In this letter, a novel motion compensation approach is developed to precisely correct the azimuth-variant motion errors. The proposed algorithm applies a time-domain filter to implement the precise Subaperture-to-Pulse correction, providing a promising azimuth-variant phase correction. Extensive experiments demonstrate the superiorities of the proposal with real-measured high-squint SAR data.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

This letter proposes a new method for focusing ground maneuvering targets and estimating the motion parameters with a synthetic aperture radar (SAR) system. In this method, the Hough transform is applied to estimate the cross-track velocity from the slope of the range walk (RW) trajectory, and the RW and Doppler centroid shift are compensated. The second-order Keystone transform is performed to correct the additional range curve caused by the along-track velocity and cross-track acceleration. Then, we adopt the adaptive polynomial Fourier transform to estimate the second-and third-order Doppler parameters from a 1-D parameter interval, and the corresponding motion parameters are calculated. Finally, the moving target is well focused after the motion parameters compensation because the second- and third-order Doppler parameters are efficiently eliminated. Both the simulated and real data processing results are presented to demonstrate the validity of the proposed algorithm.

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

Orthogonal frequency division multiplexing (OFDM) chirp waveform is considered a good choice in the waveform design for clutter suppression, which is due to its excellent characteristics such as spectral containment, phase diversity, great dynamic spectral allocation and high degree of freedom. Considering the purpose of clutter suppression, an OFDM chirp waveform design method based on multiple groups of subchirp durations optimization is proposed to improve the output signal-to-clutter-plus-noise ratio (SCNR) in this paper. The output SCNR is closely related to the waveform spectrum, so the waveforms’ energy spectral density functions are analyzed first to build the relation between the waveform parameters and spectra. Then, the multiple groups of subchirp durations optimization based on maximum SCNR is proposed and solved by an optimization method based on the sequential quadratic programming. Finally, the proposed method is verified and the optimized waveform is compared with the general waveform and the waveform with optimized single group of subchirp durations. The results show that the waveform optimized by the proposed method has higher output SCNR and the SCNR increment compared with the general waveform increases with the number of subcarriers. Besides, the high sidelobes of the general waveform are also greatly reduced.

Bistatic inverse synthetic aperture radar (Bi-ISAR) can obtain complementary information of moving targets and overcome the inherent imaging limitations of monostatic ISAR. However, the complex motion of maneuvering targets invalidates the assumption that the imaging projection plane (IPP) is constant in conventional Bi-ISAR imaging. The 2-D spatial variant phase errors would be induced. Moreover, the phase errors have a high-order form due to the time-varying bistatic angle and the high maneuvering characteristics of the target. Meanwhile, the linear geometric distortion induced by the bistatic configuration seriously challenges target identification and classification. In this paper, we propose a novel method to compensate for the 2-D spatial variant phase errors and correct the geometric distortion simultaneously for Bi-ISAR imaging based on parametric minimum entropy optimization. First, the signal mode for maneuvering target in the bistatic configuration is developed. Second, based on the developed signal model, we analyze the coupling relationship between the 2-D high-order spatial variant phase errors and the bistatic angle, and establish a parametric minimum entropy optimization model for high-order spatial variant phase errors compensation. Then, an efficient Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is adopted to obtain the optimal solution of spatial variant coefficients. Finally, with the estimated optimal parameters, the integrated processing of 2-D spatial variant phase errors compensation and distortion correction can be realized. This method can simultaneously obtain well-focused and restored Bi-ISAR images of maneuvering targets without selecting prominent scatterers. Experiments based on scattering point simulation data and electromagnetic data verify the effectiveness of the proposed method.

A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a non-negligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal model to describe the radar echoes for high-speed targets in a space-air based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity and accelerate. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intra-pulse and inter-pulse integration. A precise echo signal model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the precise echo signal model-based Keystone transform and inner-pulse chirp Fourier transform. The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space-air based bistatic radar.

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm.

Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several sparse reconstruction-based methods have been presented to solve this problem, the basis mismatch issue in sparse reconstruction theory will degrade the image reconstruction performance. To address this issue, this article proposes a novel 3-D imaging method for DLSLA 3-D SAR, which provides another idea for 3-D imaging through sparse recovery. It utilizes recovered full-sampled data to achieve cross-track dimensional imaging instead of using the under-sampled data directly as before. The Along-track-Height plane imaging is first finished by the range-Doppler (RD) algorithm and motion error compensation. Then, an advanced nuclear norm and low-rank matrix factorization (NU-LRMF)-based matrix completion (MC) algorithm and a vector reconstruction framework are built to achieve accurate recovery of full-sampled data. Finally, the cross-track dimensional imaging is completed with recovered full-sampled data by geometric correction and beamforming. Moreover, a fast two-stage iteration strategy for NU-LRMF (TS-NU-LRMF) is also presented to accelerate convergence. The robustness and effectiveness of the proposed 3-D imaging method are verified by several numerical simulations and comparative studies based on both the complex 3-D ship model and the simulated 3-D distributed scenario.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) usually uses a sparse and nonuniform linear array that often does not satisfy the Nyquist sampling theorem. Therefore, the cross-track dimensional imaging will fail with the traditional 3-D frequency-domain imaging algorithms. Several grid-based sparse reconstruction (GB-SR) algorithms have been presented to solve this issue. However, they assume that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. To address this issue, we propose a novel hyperparameter-free gridless-based sparse reconstruction (GL-SR) algorithm (i.e., 2-D adaptive iterative reweighted atomic norm minimization algorithm called 2-D IRAN) by a combination of the optimal covariance fitting criterion and atomic norm. It is a generalized model, while the other GL-SR algorithms (e.g., GLS, RGLS, and RAM) can be interpreted as the variants of 2-D IRAN. Moreover, since the interior-point method employed in toolboxes has high computational efficiency only for the small-scale matrix optimization problem, a fast implementation of 2-D IRAN via alternating direction method of multipliers (ADMM) is presented for the large-scale matrix optimization problem. Finally, we carry out extensive numerical simulations to demonstrate the advantages and effectiveness of 2-D IRAN for DLSLA 3-D SAR imaging based on the complex 3-D ship model and 3-D distributed scenario.

Fast factorized back-projection (FFBP) has significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometry and complex configuration. Conventional FFBP is generally based on the polar coordinate system (PCS) for recursive processing; however, it involves huge interpolations and causes computational inefficiency. In this article, a novel FFBP is developed for BFSAR focusing based on the Cartesian coordinate system (CCS), which is referred to as Cartesian fast factorized back-projection (CFFBP). In the new algorithm, a two-step spectrum correction is designed to avoid spectrum aliasing, and the Nyquist sampling requirement (NSR) for the BFSAR image spectrum can be decreased significantly. With low NSR in CCS, subimage merging can be implemented with noninterpolation processing, so that the proposed algorithm can achieve high performance in both accuracy and efficiency. Moreover, the practical problem of motion error is particularly considered in algorithm development, and well-adapted data-driven motion compensation (DDMC) is integrated with CFFBP based on which a new Cartesian fast time-domain (CFTD) processing framework is developed for BFSAR application. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Accurate 2-D frequency spectrum (2-D FS) with acceleration, two-way range coupling terms, and spatial-variant Doppler parameters are the main problems to be solved in translational-variant bistatic forward-looking synthetic aperture radar (SAR) (TV BFSAR) with curved trajectory. For these issues, a modified omega-K imaging algorithm is derived in this article. The maximum usage of 2-D FS based on the method of series reversion (MSR) is achieved by linear range cell migration correction, and 2-D FS is linearized in bistatic range by using high-order polynomial fitting. Then, a method of azimuth resampling is introduced to implement compensation of spatial-variant Doppler parameters. Different from other bistatic omega-K methods, our newly proposed method focuses on the small-aperture data in the azimuth frequency domain to avoid azimuth aliasing without padding zeros and uses the frequency focusing position to study the model of spatial-variant phase. Simulation results and real data verify the effectiveness of the proposed method.

In high-maneuverability bistatic forward-looking synthetic aperture radar (HMBF-SAR) imaging, the acceleration leads to an increased residual range curve and a deepened two-dimensional spatial variance of Doppler parameters, which cannot be processed by the traditional algorithms. To address these problems, this paper establishes a more accurate digital representation for HMBF-SAR model and investigates an extended azimuth nonlinear Chirp Scaling (EANLCS) imaging method. In the flowchart of this paper, we first propose a more precise slant range model with improved expansion coefficients, and defines the range and azimuth direction of HMBF-SAR imaging. Then, a novel fast reference point (i.e., azimuth and range reference point) selection method is proposed to analyze two-dimensional spatial variance of signal characteristics, which is used to construct a high order model of residual range cell migration and Doppler parameters. Based on above analysis, we put forward an advanced imaging algorithm of combining the Second-order keystone and extended azimuth nonlinear chirp scaling (EANLCS) to compensate the increased residual range curve and two-dimensional spatial variance of Doppler parameters. Finally, the effectiveness of the proposed HBMF-SAR method is verified by several numerical simulations and comparative studies based on both the simulated and raw data.

The attitude and size of satellite targets are essential information for their activity analysis. This article proposes a novel approach to estimate the absolute attitude and size of satellite targets in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation. In an ISAR image of a satellite, the satellite’s body is chosen as an individual structure segmented from each ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN). By exploring the shape feature of the satellite body with principal component analysis (PCA), the satellite attitude and size are estimated jointly through solving an optimization based on the gradient iteration method. The optimization is established by bridging range-Doppler (RD) images and the target feature parameters (attitude and size) with the accommodation of target trajectory information and the ISAR geometric projection model. In the experiments, the simulation data are generated from real satellite orbital parameters and the computer-aided-design (CAD) models of two satellite targets: TianGong (TG) and KeyHole (KH). Compared with the factorization-based reconstruction method, the proposed method can estimate the attitude and size of the satellite simultaneously and has a higher size estimation accuracy.

Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite micro-vibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blur the ISAL image. In this paper, we propose a noise-robust vibration phase compensation algorithm to accurately estimate and correct these two types of vibration phase errors by frequency descent minimum entropy optimization. Firstly, considering the characteristics of the micro-vibration of satellites, we establish a novel phase error model based on the Fourier series theory, which only contains low-frequency vibration components. The estimation of the phase errors is then translated into the estimation of the model’s Fourier coefficients, which can be achieved by a multi-dimensional minimum entropy optimization. After that, a frequency descent method (FD) is proposed to transform the multi-dimensional optimization into a group of two-dimensional optimizations so that the proposed algorithm can achieve monotonic iterative convergence. In addition, we introduce a solution space adaptive reduction operation to reduce the computational burden when solving the two-dimensional minimum entropy optimizations by the genetic algorithm (GA) to obtain the global optimal solution. Finally, experiments based on the simulated data and the real measured data confirm the effectiveness of the proposed algorithm. Compared with the traditional methods, the proposed algorithm achieves higher phase error estimation accuracy and better image quality.

This article presents a novel approach for the joint estimation of satellite attitude and size based on inverse synthetic aperture radar (ISAR) image interpretation and parametric optimization. The satellite’s solar panel, which is segmented from the ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN), is chosen for investigation in this article due to its unique rectangular structure. We innovatively use the principal component analysis (PCA) to explore the satellite solar panel’s structural features in an ISAR imagery. The projection matrix is then established to link the extracted features and the satellite’s absolute attitude and size. Parametric optimization is established based on the relationship between the extracted features and the satellite’s absolute attitude and size. A Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based fast iterative search algorithm is employed to search the satellite’s absolute attitude and size simultaneously through an iterative approach. The simulation data are generated from actual satellite orbital parameters and computer-aided-design (CAD) models of the Aqua satellite in the experiments. Simulation experiments verify the effectiveness of the proposed method.

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments.

High computational complexity and phase errors (PEs) are the main limitations of time-domain (TD) synthetic aperture radar (SAR) imaging algorithms. Accelerated fast backprojection (BP) (AFBP) algorithm avoids interpolation through wavenumber spectrum connection and is an efficient fast TD imaging algorithm. In order to deal with the image defocusing problem caused by PEs effectively and ensure rapid imaging, a TD autofocus method is proposed in this article, which is based on generalized sharpness metrics and the AFBP imaging model. The autofocus method is divided into two stages. First, for each subaperture (SA), the PE estimation model is established in unified polar coordinate (UPC), where the strong-scattering range-cell pixels are chosen to reduce memory burden and avoid repetitive imaging. The PE estimation is converted into a nonconvex optimization problem. Then, the genetic algorithm (GA) and the maximizing-maximum-pixel-value (MMPV) method are used to estimate the PEs. Second, SA images’ matching and constant PE’s compensation are performed to eliminate the residual PEs. The full-aperture well-focused image is obtained by the coherent accumulation of SA images. The effectiveness of the proposed method is proven by the results of simulation and real SAR data processing.

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with$N$-dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

An equivalent range equation with a monostatic equivalent component and a bistatic synthetic aperture radar (BiSAR) compensation component was proposed. There were five monostatic equivalent parameters (MEPs) in the both components. With the MEPs, a BiSAR range equation can be expressed in a form similar to the monostatic SAR (MoSAR) expression. Then, by using the range equation and the principle of stationary phase (POSP), we analytically derive the 2-D spectrum of a point target, and this 2-D spectrum is implemented into the range Doppler algorithm (RDA) that processes the translational invariant (TI) BiSAR data. In our RDA, spatially variant range cell migration (RCM) correction and azimuth compression (AC), both of which are related to the spatially variant MEPs, are required to focus all targets in the whole scene of different range cells. Simulations under a wide range of imaging parameters are conducted to assess the equivalent range equation and the 2-D spectrums. Satisfactory results are obtained. In addition, traditional methods for motion compensation can be directly applied to our equivalent range equation. Finally, the RDA is evaluated through the analysis of acquired raw BiSAR data. Well-focused images are obtained. Therefore, the proposed equivalent range equation, derived 2-D spectrum, and RDA have been validated for forming imagery using raw BiSAR data.

Spaceborne synthetic aperture radar (SAR) has a high application value in the observation of ship targets. After the ship is detected, the actual observation position of the moving ship and its motion parameters are worthy of concern, especially for some medium and large size valuable ships. In this paper, we propose a method of extracting the energy center of the ship signal trajectory to locate the ship first. Then according to the difference between the imaging position and the positioning position of the ship, the radial velocity estimation can be calculated. The proposed method does not need to construct a reference data box, and can directly locate the moving ship. The processing of the Gaofen-3 (GF-3) complex data verifies the effectiveness of the proposed method.

Phase gradient autofocus (PGA) is widely used in estimating residue phase error due to its efficient and robust. However, its precision severely relies on sample quality. In this paper, an expediting phase gradient autofocus algorithm is proposed to solve the above problem. First, we extract valid echo data area from the received contaminated data. Second, the optimization of the azimuth window size is presented. It gets rid of the limitation that the traditional PGA depends on the experience value. Third, the phase error can be fast calculated without numerous IFFT and zero padding which decrease computational complexity. Furthermore, the computational cost is derived in detail. Finally, Numerical simulation and raw SAR data demonstrate that the new method can achieve better performance than conventional PGA.

In microwave staring imaging, spatial-resolution of real aperture imaging is limited by actual antenna array aperture. In order to achieve high-resolution imaging of targets with sparse feature, this paper proposes a high-resolution imaging method based on temporal-spatial stochastic radiation field combining compressive sensing (CS) theory. Firstly, the formation and property of temporal-spatial stochastic radiation field are discussed. Then, signal model based on random radiation field is deduced in detail, and on this basis, high-resolution imaging method based on CS is discussed. The proposed method can distinguish targets within the beam coverage and higher quality image is achieved. Finally, numerical simulations and experiments in microwave chamber are performed to validate the method and its analysis.

Bistatic forward-looking synthetic aperture radar (BFSAR) breaks through the limitations of the conventional monostatic SAR on the forward-looking imaging. However, the problems of range cell migration (RCM) caused by the linear range walk and 2-D spatial variability of Doppler parameters become more serious and complicated in translational variant BFSAR. In this article, a keystone transform is introduced to correct the linear RCM. Based on the characteristics of a small aperture, the nonlinear chirp scaling (NCS) is discussed in the frequency domain to equalize the azimuth-range-dependent Doppler parameters. The improved NCS in our newly proposed BFSAR imaging algorithm, especially the re-definition of range direction and the model of spatial variant phase, differentiates this article from all the existing studies in the literature on BFSAR signal processing. Simulation results and real data processing further validate the effectiveness of the proposed algorithm.

In[1], the result of Fig. 13(b) was incorrectly provided. Now, we provide the corrected result, as shown inFig. 1.

In order to reconstruct the linear frequency modulated (LFM) signal, such as radar signal due to the complexity. A novel parameter estimation method based on a modified convolution kernel function (MCKF) is proposed for multi-component LFM signal in this paper. The method has fewer external cross-terms and light computational burden because of non-searching operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

Chaotic‐based random stepped frequency signal is applied in the multiple‐input‐multiple‐output through‐the‐wall detection radar (MIMO‐TWDR)recently. When the frequency modulation (FM) sequence of transmission signal is controlled by the chaotic signal, the single‐frequency interference such as the power harmonics sneaking into the phase detector becomes periodical and therefore can be filtered in frequency domain. However, the target echo signal becomes random after chaotic modulation, where the matched filter usually is unable to be realised by Fourier transform and consequently the envelope of direct wave after the phase detector varies stochastically and is difficult to be eliminated by an analogue filter. The FM sequence of random disorganising cannot meet the demand of filtering out the single‐frequency interference and direct wave simultaneously. Therefore, a new method of FM sequence optimisation of chaotic‐based random stepped frequency signal based on genetic algorithm is proposed to solve these problems in this study. Simulations show that the optimised stepped frequency signal possesses the advantages of both chaotic‐based random and linear stepped frequency signal. The proposed scheme achieves excellent performance on direct wave and single‐frequency interference suppression and target detection. Moreover, it can avoid the interference between transmission antennas of MIMO radar.

In this paper, an interpolation-free fast factorized back-projection (IF-FFBP) algorithm is proposed for high-resolution spotlight synthetic aperture radar (SAR) processing. Different from the original FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, IF-FFBP finishes the image merging steps using chirp-z transform and circular shifting. Under the restriction of the applicable scope, IF-FFBP yields enhanced efficiency over the 4 times upsampling interpolation based FFBP, and keeps the high precision simultaneously. Finally, Real-data experiment verifies the efficiency superiorities of the FIM-FFBP.

Narrow angle approximation and isotropic assumption adopted in regular radar imaging are violated in wide angle radar imaging scenario. Therefore, conventional Fourier based methods are not directly applicable, and full aperture algorithms perform poor under low SNR. This paper proposes an imaging scheme based on Sparsity Enhanced Non-negative Matrix Factorization (SENMF). The full aperture is firstly divided into several overlapping subapertures, to which Polar Format Algorithm (PFA) is then applied to obtain subimages at different aspects. Finally, NMF with sparsity regularization is exploited to iteratively composite the full aperture image, which demonstrates enhanced target feature and improved SNR. The results of Backhoe data processing verify the validity of the novel approach.

The amplitude‐and‐phase error (APE) between phased array channels is notorious in radar signal processing. This error can cause an inaccurate estimate of unknown steering vector of the target echo signal and eventually result in amplitude‐phase discontinuity of the phased array output. Thus, how to handle the APE is a meaningful problem, particularly for the varying jamming environment, of which the signal‐to‐noise ratio is very low. In this study, the authors have developed a new method to obtain real‐time amplitude and phase differences between two consecutive weight update periods based on the accurate estimation of steering vector. Such differences can be used to obtain a real‐time weight vector with negligible amplitude‐phase distortions, and hence improves the phased array signal‐processing performance. The proposed method is very flexible: it works well in different array configurations, such as linear, rectangular and Y‐shape arrays, and can be efficiently implemented in any eigenstructure‐based direction‐of‐arrival system.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.

In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

In this letter, a novel approach to autofocus for inverse synthetic aperture radar (ISAR) imaging called minimum entropy via subspace autofocus is presented. This scheme uses the weighted signal subspace to express the phase errors left in the echoes after range-bin alignment and estimates the optimal weights sequentially via an optimization algorithm based on an entropy minimization principle, and its robustness and convergence can be ensured by the optimization method. Both the theoretical analysis and processing results of the real ISAR data have confirmed the feasibility of this new scheme.

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.