In order to address the low radar-cross-section target detection challenge under high sea states and remove the dependence of existing methods on idealized prior knowledge, a two-stage closed-loop adaptive pulse-agile radar framework is proposed in this letter. First, the clutter and target parameters are estimated with the expectation-maximization scheme, followed by the processing of principal component analysis-based clutter suppression and generalized likelihood ratio test detection. Then, the multi-pulse polyphase-coded frequency modulate waveform is designed to minimize the complementary integrated sidelobe level in strong clutter regions with the iterative method. Numerical experiments are conducted to demonstrate the advantage of the proposed method in clutter suppression. To further confirm the robustness of our method, we also present the Monte Carlo experiments in challenging sea condition.

Transformer-based architectures exhibit substantial promise in the realm of ultra-high-definition (UHD) image restoration (IR). Nevertheless, they encounter significant challenges in maintaining high-frequency (HF) details, which are crucial for the reconstruction of texture. Conventional methods tackle computational complexity by significantly reducing the resolution (by a factor of 4 to 8). Moreover, the majority of high-frequency components are eliminated due to the inherent characteristics of self-attention mechanisms, as these mechanisms tend to naturally suppress high-frequency elements during non-local feature integration. This paper proposes a dual-branch transformer architecture that synergistically combines native-resolution HF preservation with efficient contextual modeling, named HiFormer. The high-resolution branch utilizes a directionally-sensitive large-kernel decomposition to effectively address anisotropic degradations with fewer parameters and applies depthwise separable convolutions for localized high-frequency (HF) information extraction. Concurrently, the low-resolution branch assimilates these localized HF elements using adaptive channel modulation to offset spectral losses induced by the inherent smoothing effect of self-attention. Comprehensive experiments across numerous UHD image restoration tasks reveal that our approach surpasses current leading methods in both quantitative metrics and qualitative analysis. The code is available at https://github.com/5chen/HiFormer.

Registration of optical and synthetic aperture radar (SAR) remote sensing images serves as a critical foundation for image fusion and visual navigation tasks. This task is particularly challenging because of their modal discrepancy, primarily manifested as severe nonlinear radiometric differences (NRD), geometric distortions, and noise variations. Under large geometric transformations, existing classical template-based and sparse keypoint-based strategies struggle to achieve reliable registration results for optical-SAR image pairs. To address these limitations, we propose GDROS, a geometry-guided dense registration framework leveraging global cross-modal image interactions. First, we extract cross-modal deep features from optical and SAR images through a CNN-Transformer hybrid feature extraction module, upon which a multi-scale 4D correlation volume is constructed and iteratively refined to establish pixel-wise dense correspondences. Subsequently, we implement a least squares regression (LSR) module to geometrically constrain the predicted dense optical flow field. Such geometry guidance mitigates prediction divergence by directly imposing an estimated affine transformation on the final flow predictions. Extensive experiments have been conducted on three representative datasets WHU-Opt-SAR dataset, OS dataset, and UBCv2 dataset with different spatial resolutions, demonstrating robust performance of our proposed method across different imaging resolutions. Qualitative and quantitative results show that GDROS significantly outperforms current state-of-the-art methods in all metrics. Our source code will be released at: https://github.com/Zi-Xuan-Sun/GDROS.

The interrupted sampling repeater jamming (ISRJ) is a widely used coherent jamming technique. A proper waveform design can effectively suppress or mitigate the ISRJ. Even if several phase-coded waveform design methods have been proposed for this purpose, frequency modulation (FM) waveforms remain the most common choice for high-power transmitters, as they do not introduce significant distortions in real radar systems. In this letter, we propose a design method for multiple-input multiple-output (MIMO) radar that simultaneously derives the optimal polyphase-coded FM (PCFM) waveforms and the receive filters to mitigate the ISRJ. Specifically, we first model the joint design problem as a nonconvex bivariate optimization problem and we minimize the matching error between the desired and practical transmit-receive correlation functions for different channels. Subsequently, we adopt an alternating strategy to update the PCFM waveforms and receive filters sequentially. More specifically, gradient-based algorithms in Euclidean space and Riemannian manifold space are adopted to derive the optimal waveforms and filters, respectively. The proposed method is characterized by a low computational cost, thanks to its FFT-based implementation. Numerical analysis shows the effectiveness of the proposed method.

Registration of optical and synthetic aperture radar (SAR) image pairs is a fundamental task in various remote sensing applications, including image fusion, target localization, and object detection. Unlike homogeneous image pairs, optical and SAR image pairs exhibit a significant modality gap, making it exceptionally challenging to extract consistent and reliable features. Particularly for optical and SAR image pairs with substantial geometric differences, few methods can achieve high-precision registration. To address this challenging task, we introduce a novel registration framework, called OS3Flow, leveraging on the implicit symmetry between heterogeneous image pairs to extract high-quality semi-dense flow estimations. We start by training the network in a multi-task manner using a standard flow regression loss as well as a symmetry loss with reverse input order. A confidence mask thus can be generated to measure the similarity between predictions at inference time. We then perform a linear regression upon selected flows with high confidence to estimate the parameters of underlying affine transformation. Under large transformations, our proposed method achieves an average registration error of less than 3 pixels on the public OS dataset and WHU-OPT-SAR dataset, demonstrating superior accuracy and robustness compared to state-of-the-art methods.

This article focuses on the multiazimuth angle interpolation task of inverse synthetic aperture radar (ISAR) images for aircraft targets and complements incomplete ISAR image datasets. ISAR image automatic target recognition (ATR) has been widely applied in remote sensing and many fields. However, the imaging process is more challenging when compared to capturing optical and SAR image data, which reduces the accuracy and generalization performance of the ATR system. Therefore, in this article, we leverage existing limited ISAR data to achieve autonomous data expansion. This approach helps mitigate the impact of low sample quantity and unbalanced distribution, ultimately improving the accuracy of the ATR system for target recognition. Most existing methods use generative networks for ISAR image expansion, but few focus on generating ISAR images with specific azimuth angles. This article proposes a novel two-stage coarse-to-fine framework for ISAR object view interpolation (C2FIPNet) that combines flow estimation and GAN to interpolate ISAR images with intermediate azimuth angles using a set of ISAR image pairs. Flow estimation is employed for coarse-grained generation, determining the position and intensity of strong scattering points in the ISAR image. The GAN, on the other hand, is used for fine-grained completion to correct image distortion caused by flow estimation and enhance image details. In addition, a suitable loss function is designed, incorporating both global and local features, allowing for priority generation in the region of strong scattering points. In conclusion, extensive simulation and comparative experiments have demonstrated that the interpolated ISAR images generated by the proposed C2FIPNet exhibit greater pixel-level authenticity.

Non-cooperative bistatic radars do not transmit electromagnetic waves but use emitters of other host radars as illuminators for target detection. If the non-cooperative bistatic radar and the host radar belong to the opposing camps respectively, the non-cooperative bistatic radar will be a great challenge to the host radar. To ensure the electromagnetic information security of host radars, a joint design of host radar waveforms and mismatched filters is proposed to reduce the detection performance of non-cooperative bistatic radars. For non-cooperative bistatic radars, we raise the autocorrelation function sidelobe levels of the transmitted waveform to deteriorate its detection performance. For host radars, we design a mismatched filter to improve the target detection performance of such transmitted waveforms. The simulation results show that the effects of raising and suppressing the range sidelobes in the model are both achieved. The price is additional SNR loss (about 2dB). Two types of transmission waveforms are designed including main lobe broadening waveforms and false peaks waveforms, by controlling the autocorrelation function elevated position parameter. The constant false alarm detection results show that the designed waveforms fulfill the requirements of non-cooperative bistatic radar countermeasures.

This letter presents a radar waveform design for deceiving noncooperative bistatic radars (NCBRs). The principle of deceiving the noncooperative bistatic radar is proposed according to some imperfections in the engineering realization of NCBRs whose direct wave parameters are selected from the template library to reconstruct the reference signal. These imperfections are either unavoidable, such as phase asynchrony, or hard to find, such as time errors. For the first time, a radar waveform design method based on joint modulation of the initial phase and the pulse repetition interval (PRI) is proposed to achieve range migration–velocity deception for NCBRs. When our host radar illuminates our key target, the noncooperative bistatic radar will detect our key target. The proposed waveform will mislead the target detection results of the noncooperative bistatic radar in range–time (R–T) maps and range-Doppler (R-D) maps. It can not only be used to disguise a moving target as a stationary point target to avoid moving target detection (MTD) of NCBRs, but also modulate the target echo velocity into the opposite direction of the real motion to prevent our key moving targets from being tracked. Both the simulation data and the measured results show that the deception method proposed in this letter fulfills the requirements of NCBR countermeasures

Precession is a typical form of micro-motion that can bring about complex and time-varying Doppler modulation. The range instantaneous Doppler (RID) method, which uses time-frequency analysis instead of the Fourier transform to describe the time-varying Doppler, is typically used to obtain the high-resolution inverse synthetic aperture radar (ISAR) image of a precession target. However, the observation time of a specific target is often non-uniform due to various interference and channel switching among multi-channel radars, which will lead to a sparse aperture. Sparse aperture can cause sidelobe interference in the ISAR image obtained by the RID method, making it difficult to focus well. To solve the problem whereby the RID method fails to image a precession target with sparse aperture, this paper proposes a new method based on the joint constraints of low-rank and sparsity of tensor, and uses the alternating direction method of multipliers to solve the problem. The low-rank can constrain the correlation among consecutive ISAR images, and sparsity can remove the impact of sparse aperture. This effectively eliminates the micro-Doppler interference and sidelobe interference in ISAR image, and enables the reconstruction of precession target in a sequence of ISAR images with sparse aperture. Experimental results under both simulations and darkroom measurements verify that the proposed method performs well on ISAR images of conic precession target with sparse aperture.

Waveform adaptation grants cognitive radar (CR) the ability to adapt to its environment, which requires an effective framework to synthesize waveforms sharing a desired ambiguity function (AF). In this letter, we propose a novel method for shaping the slow-time AF in order to adaptively suppress the interference power. The problem is formulated as minimizing the average value of the slow-time AF over some range Doppler bins spanned by the interference, which can be identified exploiting a plurality of knowledge sources. From a technical point of view, this is tantamount to optimizing a complex quartic-order polynomial with a constant modulus (CM) constraint on each optimization variable. To solve this problem, we proposed a quartic Riemannian trust region algorithm. This algorithm first transforms the optimization into an unconstrained one in a complex circle Riemannian manifold, then devises a new Riemannian trust region optimization algorithm that invokes Riemannian gradient and Hessian matrix to obtain an iterative solution with super-linear convergence rate and ability to escape potential saddle points. Simulation results demonstrated that our proposed algorithm outperformed state-of-the art approaches for AF shaping while being computationally less expensive.

This study proposes a model-driven deep network based on the linear alternating direction method of multipliers (L-ADMM), to solve the problem whereby the inverse synthetic aperture radar (ISAR) generates defocused images of targets exhibiting micro-motion with sparse aperture. The network unfolds the operation process of L-ADMM into a model-driven deep network, and automatically optimizes the parameters of the network through learning instead of manually adjusting the parameters, which can better obtain images. Analyses of data acquired through simulations and experimental measurements were used to compare the results of imaging obtained by L-ADMM-net with those of the range Doppler (R-D) algorithm, chirplet algorithm, and L-ADMM. The entropy of images obtained by L-ADMM-net was the lowest, and their image contrast and resolution were the highest. Moreover, L-ADMM-net can generate high-resolution images of targets exhibiting micro-motion with sparse aperture at a low signal-to-noise ratio (SNR), which verifies its robustness. It can also automatically update and adjust parameters more stably than L-ADMM. The proposed method significantly improves the resolution, robustness, and stability of images of targets exhibiting micro-motion in different situations compared with traditional methods, and can provide technical support for target recognition in the future.

Micro-Doppler (m-D) signatures induced by micro-motion dynamics, which are of great importance for target classification, have received increasing attention among the radar community. For scenarios when the micro-motion of radar target is accompanied by macro-motion, the periodicity of m-D is disturbed by macro-motion. In that case, extraction of micro-motion signatures based on the periodicity of time-frequency representation (TFR) of the radar echo may become invalid. In this work, we show that the periodicity of TFR is replaced by circular periodicity in the presence of macro-motion. In view of this, the circular correlation coefficients of TFR are employed to characterize the circular periodicity of TFR and to provide an estimate of the micro-motion period. The property of circular correlation coefficients enables us to estimate the micro-motion period of radar targets in the presence of macro-motion. Experiments with synthetic data and measured radar data validate the effectiveness of the proposed method.

Multi‐input–multi‐output (MIMO) radar can fundamentally improve radar performance by using diversity technique. A group of specially designed signals, which usually are orthogonal, are required to be transmitted for MIMO radar obtaining excellent diversity performance. Although some well orthogonal codes have been provided, they are mostly Doppler sensitive, and their side‐lobes level also need to be improved. In this study, the poly‐phase codes model is presented and the optimisation problem is then analysed. An adaptive clonal selection algorithm is proposed to numerically optimise such poly‐phase coded orthogonal signals. To obtain low level of aperiodic autocorrelation side lobe and cross correlation as well as good Doppler shift tolerance, sustainable Doppler shifts are introduced into the optimisation course. Numerical simulation results show the superior correlation and Doppler tolerance performances comparing with some well known codes.

To have a further understanding of the recently developed space-time adaptive processing (STAP) methods based on sparse representation (SR-STAP), this letter details the clutter sparsity observed by STAP radar systems. First, we review the principle and discuss the existing problems about clutter sparsity of the SR-STAP-type algorithms. Then, a theoretical analysis on clutter sparsity for a side-looking uniform linear array with constant pulse repetition frequency, constant velocity, and no crab is performed. Some important conclusions are obtained, and simulations are used to validate the correctness of them.

Fast rotating targets such as gimbaled antennas or propeller blades may cause migration through resolution cells (MTRC) of the high-resolution range profile during the imaging time, which makes the inverse synthetic aperture radar image smeared. To solve this problem, a novel imaging method for fast rotating targets is proposed in this paper. The method is based on the segmental pseudo Keystone transform, which is designed to realize MTRC correction. The fast realization is achieved by employing the discrete match Fourier transform, which makes the algorithm feasible and simple. Experiments with simulated radar data demonstrate the performance of the proposed method.

In radar target imaging, motion induces a range-Doppler coupling effect, which results in distortion in synthesizing a range profile for a moving target. To eliminate or suppress the distortion of the synthetic range profile, conventional technology such as motion compensation requires velocity estimation. Unfortunately, for a high-speed moving target, it is difficult to achieve real-time accurate estimation of the velocity of the target. Based on phase cancellation, a new technology is proposed to achieve a motion target range profile via stepped-frequency waveform. The new technology does not need the estimation of the velocity. Hence, its computational cost can be minimized. It is also easier to use. The simulation result confirms the effect of the new technology