Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

The joint observation of multiple sensors is the main means for synchronously obtaining large-coverage, high-precision, and multi-scale ocean wind and wave information. The higher the synchronization of these data in time and space, the more conducive to improving the accuracy of wind and wave information. Therefore, this paper proposes an idea of simultaneously realizing the three functions of spectrometer, scatterometer and SAR by sharing a phased-array antenna, and a very small receiving antenna is also carried to enhance the flexibility of time sequence. The constraints on the time sequence of the pulse transmission and reception for the three functions working simultaneously are derived in detail. Subsequently, a joint design method for the pulse repetition frequencies (PRFs) of three functions is introduced, and simulation verification is carried out by zebra diagrams.

This paper focuses on developing the processing algorithm applicable to the GEO SAR especially at high squint. The analytic expressions of the squint-mode wavenumber support and point spreading function are derived. Then, we propose a generalized fast Cartesian factorized back-projection algorithm, which can deal with high-squint data, and the scene coordinate system can be built with high flexibility. Especially, the optimal scene coordinate system is exploited. Finally, processing results of simulated data are presented to validate the proposed algorithm.

In recent years, the back-projection (BP) algorithm has shown good potentials in SAR focusing with nonideal conditions like nonideal trajectory and nonuniform sampling. However, when applying the original BP algorithm directly to periodic non-uniform sampling data, ghostings can be introduced into the imaging results, leading to image quality distortion. To address this issue, a novel reconstruction method based on joint sub-channel BP images is proposed. We first analyze the causes and properties of Doppler grating lobes and BP ghostings in the periodic non-uniform signals. Based on the analysis, a reconstruction process of the joint sub-channel BP images is established to achieve the purpose of suppressing ghosting. The proposed method can be smoothly combined with the Ground Cartesian back-projection (GCBP) algorithm to realize high computational efficiency. Finally, the simulation and measured data are presented to verify the effectiveness of the algorithm.

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of participants, FL still can't resist inference attacks against model parameters and the single point of failure of the central server. In addition, malicious clients can disguise as benign participants to launch poisoning attacks, which seriously compromises the accuracy of the global model. In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. The framework can effectively avoid a single point of failure and resist inference attacks. In particular, it can limit the success rate of poisoning attacks to less than 26%. Moreover, the framework alleviates the storage pressure of the blockchain, achieves a balance between privacy budget and global model accuracy, and can effectively resist the negative impact of node withdrawal. Simulation experiments on image datasets show that the BFG framework has a better combined performance in terms of accuracy, robustness and privacy preservation.

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.

Geosynchronous-Earth-orbit (GEO) synthetic aperture radar (SAR) provides excellent continuous observing capability and large swath. However, the extremely long synthetic aperture time, the curved orbit, and the nonplanar ground surface cause serious spatial variance in the GEO SAR signal. In this article, a novel fast Cartesian back-projection (BP) algorithm based on subaperture imaging on ground and multistage fusion is proposed for accurately and efficiently imaging of GEO SAR. The imaging grids are arranged on the ground surface to avoid the azimuth defocusing caused by the flat ground approximation. Then, a new two-step spectrum compression method is derived to solve the spectrum aliasing of subaperture images. Also, a multistage image fusion method is adopted to combine all the subaperture images with high efficiency. The computational complexity and the approximation of the proposed algorithm are also discussed. Simulation results verify the effectiveness of the proposed algorithm.

Airborne synthetic aperture radar (SAR) with an imaging resolution of up to 0.03 m is developed. However, the imaging process suffers from motion errors with 2-D spatial-variant characteristics that invalidate approximations suitable for motion compensation (MOCO) in a submeter resolution SAR system. To estimate and compensate for 2-D spatial-variant motion error (2-D SVME), we propose a novel two-stage processing framework for the ultrahigh-resolution microwave photonic (UHR MWP) airborne SAR imaging. In the first stage, the two-step MOCO compensates for the spatial-invariant and range-variant motion errors. Range downsampling and azimuth windowing are adopted to increase the robustness of the method. Afterward, the coupling of the 2-D SVME is greatly decreased, and a coarse-focused image is obtained. In stage two, an extended autofocusing method in the 2-D wavenumber domain based on the extended range migration algorithm (ERMA) compensates for the azimuth-variant motion errors and nonsystematic range cell migration (NsRCM) for 2-D wide-swath stripmap SAR data. After the ERMA and obtaining the coarse-focused image, the analytical structure of the residual 2-D phase error in the wavenumber domain is revealed. A nonlinear scaling equation is developed, thus relating the 1-D azimuth phase error to the 2-D phase error correction. The Ku-band stripmap UHR MWP (0.03 m) airborne SAR data are analyzed to verify the necessity and effectiveness of the proposed framework. A well-focused stripmap SAR image is obtained.

The existing microwave remote sensors for ocean wave observation, such as SAR and spectrometer, have their respective specialties through different measuring mechanisms. However, every individual sensor shows obvious limitations when considering advanced wide-swath complete-elements ocean wave observation. This paper proposes an integrated microwave remote sensor for double-mode ocean wave observation, and designs the main system parameters and the joint time sequence. The double-mode integrated microwave remote sensor adopts a digital array antenna, but functions as a spectrometer and a SAR simultaneously through multiple beam forming technology. The proposed double-mode integrated microwave remote sensor not only realizes simultaneous, wide-swath, relatively complete-elements, and high-accuracy observation, but also reduces the cost, and compared to traditional single satellite multi-payload systems, it is more probable.

This paper proposes to apply the local-plane model in urban tomography imaging to increase the detection probability and the regularity of the persistent scatterers (PSs). A local-plane generalized likelihood ratio test (LP-GLRT) algorithm is developed, which shows a better adaption to the nonplanar architectures and terrain when compared with the Multi-look GLRT algorithm. Experiments on Terra-SAR images are presented to validate the algorithm.

This paper deals with the combination of multi-pass images obtained by COSMO-SkyMed SAR satellite over the urban area of Napoli. The coherent processing can improve geometric resolution and the image quality of long-term coherent regions. At last, the coherently and incoherently combined images are fused tighter based on the coherence to increase the readability of the low-coherence regions.

Ship motions during long coherent processing interval (CPI) have six degrees of freedom, and the oscillatory motions are roughly periodical. The traditional ship imaging methods usually use a short time interval to form an image, while the image quality may suffer from low resolution, poor signal-to-noise ratio (SNR), and scatter scintillation. Using a longer CPI to generate an image may improve the quality but, however, largely increase the focusing difficulty. In this article, we investigate the focusing challenges of oscillatory ships with long CPI. Through analyzing the relative motion between the radar and the ship, the properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory ship imaging are studied. It is illustrated that the WDS is a 3-D sparse curved surface generated by the complex relative motion, with a time-variant energy density, nonparallel spectrum boundaries, and a complex structure. The PSF of an oscillatory ship may have a 3-D resolution but also multiple high-level sidelobes. The relationship between the WDS and the nonideal PSF is illustrated with the projection slice theorem (PST). Moreover, it is discussed that the scatterers distributed on a 3-D ship cannot be focused uniformly on a 2-D imaging plane (IP) due to the variation of the slant-range plane (SRP). The projection relationships of the resolutions and focusing positions between the SRP and the IP are also derived. Simulation results are presented to validate the analyses throughout this article.

Since path curvature becomes severer for higher orbit synthetic aperture radar (SAR), the stripmap mode may not provide a reliable azimuth resolution under different look angles or at different positions. Beam steering is especially valuable herein for adjusting the azimuth resolution under different observation conditions by designing the antenna steering rate. Moreover, considering that the large range migration and center range variation in the squint mode may increase the echo length and reduce the achievable scene width, we proposed a novel 2-D beam steering (TDBS) method, which promises not only a required azimuth resolution but also a wide swath (or shortened echo length) at squint when cooperated with the variable interpulse time (VIPT) technique. The simulation results obtained under different look directions are shown to validate the effectiveness of the proposed beam controlling method.

The curved trajectory and long synthetic aperture time of medium-earth-orbit (MEO) synthetic aperture radar (SAR) lead to a two-dimensional spatial variation in the signals. Traditional methods treat the range and azimuth variations separately, and usually suffer from high computational complexities. We investigate the Doppler rate distribution across a large scene, and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. The additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be improved. Finally, processing of simulated stripmap-mode data with 2-m resolution can validate the proposed algorithm.

There are two technical difficulties to overcome before obtaining a well-focused image from high squint (HS) synthetic aperture radar (SAR) with constant acceleration. One is effective range modeling and the other is the correction of space-variant (SV) Doppler parameters. Based on the imaging characteristics analysis, an orthogonal expansion range model (OERM) is proposed which can handle the coordinate rotation caused by range walk correction (RWC). Then a modified spectral analysis (SPECAN) with the Doppler resampling method is designed to correct the SV Doppler parameters. Finally, the proposed algorithm is verified by both simulated and real SAR data. Meanwhile, it shows an improvement in azimuth focusing quality over the reference one.

The curved trajectory and long synthetic aperture time of medium-Earth-orbit (MEO) synthetic aperture radar (SAR) lead to a 2-D spatial variation in the signals. Traditional methods treat the range and azimuth variations separately and usually suffer from high computational complexities. In this article, we investigate the Doppler rate distribution across a large scene and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. Thus, the additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be obviously improved. The Doppler linearization is used to address the higher-order Doppler parameters to achieve more precise focusing, and at the same time, addresses the azimuth time shift caused by the changes of signal distribution. Finally, processing results of simulated stripmap-mode data with the 2-m resolution are presented to validate the proposed algorithm.

Ship oscillatory motions with long coherence processing interval (CPI) are complex and hard to accurately estimate. The traditional ship imaging methods usually avoid long-CPI focusing by dealing with a short observation time interval, which may make it hard to obtain a high-resolution and high-SNR image. In this paper, the mechanisms and challenges of long CPI imaging of oscillatory targets are investigated. The properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory targets are analyzed. It's found that the WDS spreads as a three-dimensional (3D) thin and curved sweep surface, with time-variant energy density, non-parallel boundaries and a complex structure. The PSF of an oscillatory target has a 3D resolution, but also multiple side-lobes with high level. We inspect the relationships between the properties of the WDS and the non-ideal PSF. Moreover, it's found that scatters distributed on a 3D oscillatory target cannot be focused uniformly on a predefined imaging plane (IP). The projection relationship of the target focusing positions on the slant-range plane (SRP) and the IP are also derived. The simulation results can well validate the proposed method.

A squinted observation geometry along with long integration time significantly aggravates the range walk and spatial variation of a medium-earth-orbit (MEO) synthetic aperture radar (SAR) signal. Variable pulse repeating frequency (PRF) is recommended to avoid the blockage in echo recording and save storage space. The existing wavenumber algorithms cannot handle the nonlinear and range-azimuth-coupled spatial variation (RACSP) over a large scene. In this paper, we propose a modified Stolt mapping method along with a modified joint time and Doppler resampling (JTDR) for highly squinted MEO SAR data processing. An azimuth timescale transformation is used to deal with the nonlinear spatial variation of the azimuth frequency-modulation (FM) rate. An extended Omega-K is used to linearize the range frequency and achieve range cell migration correction (RCMC). To address the RACSP, the Doppler is linearized in the range-Doppler domain using a range-dependent Doppler scale transformation. The computational complexity and geometry distortion correction (GDC) are also discussed. Simulation results are shown to verify the effectiveness of the developed focusing approaches.

The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.

Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

Signal bandwidth and integration time are two significant parameters of a geosynchronous synthetic aperture radar (SAR) sensor. They directly determine the resolution characteristic of SAR imagery. Because their contributions to the ground impulse response width (IRW) curve (consists of −3-dB resolutions in every direction) are severely coupled, an analytical extraction method of the ground IRW curve is studied to analyze the coupling characteristic. Due to the coupling, the IRW curve is generally spatially variant and, thus, can degrade the quality of SAR imagery. To minimize the variation, the two parameters are optimized. However, the optimized signal bandwidth is found to be satellite position varied, which complicates the system design. To solve this problem, a parameter optimization model is built to obtain one optimal signal bandwidth as well as to decrease the variation.