Synthetic Aperture Radar (SAR) imaging relies on using focusing algorithms to transform raw measurement data into radar images. These algorithms require knowledge of SAR system parameters, such as wavelength, center slant range, fast time sampling rate, pulse repetition interval, waveform, and platform speed. However, in non-cooperative scenarios or when metadata is corrupted, these parameters are unavailable, rendering traditional algorithms ineffective. To address this challenge, this article presents a novel parameter-free method for recovering SAR images from raw data without the requirement of any SAR system parameters. Firstly, we introduce an approximated matched filtering model that leverages the shift-invariance properties of SAR echoes, enabling image formation via convolving the raw data with an unknown reference echo. Secondly, we develop a Principal Component Maximization (PCM) method that exploits the low-dimensional structure of SAR signals to estimate the reference echo. The PCM method employs a three-stage procedure: 1) segment raw data into blocks; 2) normalize the energy of each block; and 3) maximize the principal component's energy across all blocks, enabling robust estimation of the reference echo under non-stationary clutter. Experimental results on various SAR datasets demonstrate that our method can effectively recover SAR images from raw data without any system parameters. To facilitate reproducibility, the Matlab program is available at https://github.com/huizhangyang/pcm.

This paper utilizes a closely-deployed active reconfigurable intelligent surface (RIS) to assist the transmit array for significantly reducing sidelobe levels without a mainlobe gain loss. To avoid pre-specifying a sub-optimal pattern mask, we jointly design the transmit weights and active RIS reflection coefficients by maximizing the ratio between minimal mainlobe gain and peak sidelobe level. Under the constraints of transmit power, total power consumption and maximum amplification factor of active RIS, and acceptable mainlobe ripple, we formulate the proposed beampattern shaping into a nonconvex constrained optimization problem. Then we customize an effective algorithm based on the alternating direction method of multipliers (ADMM) framework to tackle variable-coupled nonconvex constraints, wherein we specially solve the subproblems related to the transmit weights and active RIS reflection coefficients by leveraging consensus ADMM and quadratically constrained quadratic program with one constraint techniques simultaneously. Numerical results show that the proposed array yields markedly lower sidelobe levels and a slightly higher mainlobe gain than conventional and passive RIS-aided ones. The mechanism is that the beampattern component yielded by the reflection path has almost opposite phase and identical amplitude in the sidelobe region as that of the direct path, dramatically canceling the sidelobes.

To improve the multi-target detection ability of an active array radar in interference environments, we deploy an reconfigurable intelligent surface (RIS) near the active array to assist the radar transmitter. By considering the minimum signal-to-interference-plus-noise ratio (SINR) among all targets as the metric, we jointly optimize the RIS reflection coefficients as well as the radar’s transmit and receive beamformers under the constant modulus constraints on the RIS reflection coefficients and phase-only transmit beamformer. To tackle the resultant problem, we decompose it into three sub-problems under the alternating optimization framework, and separately address these sub-problems through generalized Dinkelbach transform, majorization-minimization, and minimum variance distortionless response methods. Numerical results demonstrate that the assistance of RIS effectively enhances the illumination power of radar transmitter towards all targets, thereby improving their SINRs.

Hybrid analog-digital (HAD) architecture has emerged as a crucial technology in modern wireless communication and radar systems, offering a balance between system performance and hardware cost. This paper investigates the beampattern shaping of HAD arrays without requiring a pre-specified pattern mask, aiming to precisely control the mainlobe and sidelobe shape by maximizing the ratio of minimum mainlobe level to peak sidelobe level. The resulting problem is a tightly coupled and highly nonconvex biquadratic fractional program, subject to uni-modulus constraints on the analog weights and a total power constraint on the digital weights. To tackle this challenge, we introduce bounded auxiliary variables to convert the fractional objective into integral forms, and develop an alternating direction penalty method (ADPM) with automatically regulated penalty parameters to decouple the updates of analog and digital weights. Specifically, the analog weights are efficiently updated by the Riemannian Newton method (RNM) and the digital weights are solved by the Lagrange multiplier method. Both the convergence of ADPM and the local convergence condition of RNM are explicitly derived. Numerical results demonstrate that the proposed algorithm achieves comparable beampattern performance to fully digital arrays, making it highly suitable for practical radar and communication applications.

Pulse-Doppler (PD) radars, which prefer to operate with large bandwidth signals to attain superior range resolution, are widely used due to their superior performance in target detection and parameter estimation. However, the increasing data flow of such radar systems induced by large bandwidth presents significant challenges to signal processing and hardware implementation, particularly concerning analog-to-digital converters (ADCs), which are tasked with converting high-bandwidth inputs into digital representations at or above the Nyquist rate. In this work, we introduce a hybrid analog-digital processing architecture, which implements sub-Nyquist sampling as well as low-bit quantization, specifically tailored for PD radar applications. We refer to the designed architecture as the bit-limited sub-Nyquist pulse-Doppler radar (BiLiPD) receiver, designed with cost-efficiency in mind, utilizing low-rate and low-resolution ADCs to reduce both cost and power consumption. Specifically, the received radar echoes are acquired under the sub-Nyquist sampling framework and then quantized with low-resolution ADCs. The building modules of the proposed BiLiPD receiver, including the analog pre-processing, the ADCs, and the digital processing, are jointly designed to mitigate the challenges posed by sub-Nyquist sampling and low-bit quantization. We incorporate structural constraints into the design problem, formulating it under the task-based quantization framework and employing gradient descent methods for its solution. Our simulation results illustrate that the proposed BiLiPD receiver operating under low-rate low-bit constraints is capable of accurately recovering target parameters with its target recovery performance approaching that of classic PD processing operating with unlimited resolution ADCs while notably outperforming that employing task-ignorant low-bit quantization.

To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring.

This article proposes a control strategy using a prescribed-time tracking control technique to address the challenges of achieving high-precision trajectory tracking control for USVs. The scheme considers marine environmental disturbances, model uncertainties, and unmodeled dynamics while imposing prescribed performance restrictions. Initially, a prescribed-time extended state observer is established to rapidly and precisely reconstruct unmeasured velocity and the lumped disturbances, including marine disturbances, model uncertainties, and unmodeled dynamics. Furthermore, a novel prescribed-time prescribed performance function is created to improve transient and steady-state performance. Then, a prescribed-time tracking control scheme utilizes the extended state observer technique, prescribed performance constraint, prescribed-time stability theory, and dynamic surface control algorithm to ensure that the tracking errors converge rapidly and precisely within the prescribed time while remaining within the predefined performance constraints. Additionally, the Lyapunov stability theory demonstrates the system's prescribed-time stability. In conclusion, simulation experiments are performed to validate the efficacy and superiority of the suggested control strategy.

Limited by the number of antenna elements, conventional array radars usually have insufficient ability of signal-dependent interference suppression in harsh environments, even though transmit and receive beamformers are jointly designed. This article deploys an active reconfigurable intelligent surface (RIS) to assist the receive array, which provides numerous extra degrees-of-freedom to suppress interferences and to enhance the beamforming gain of target echo simultaneously since the active RIS has the capability of adjusting and amplifying incident signals. Aiming to maximize the output signal-to-interference-plus-noise ratio (SINR), we jointly design the phase-only or power-limited transmit beamformer, receive beamformer, and active RIS reflection coefficients. We devise an alternating optimization-based algorithm to handle the resultant nonconvex-constrained fractional programming problem. Specifically, the phase-only transmit beamformer is determined by the Riemannian gradient descent (RGD)-based method while the power-limited one is given as a closed-form optimal solution, and the active RIS reflection coefficients are updated by the concave-convex procedure (CCCP)-based method. Moreover, we derive the convergence condition of the proposed algorithm based on the properties of RGD and CCCP. Numerical results reveal that the proposed active RIS-aided array radar significantly outperforms the passive RIS-aided and RIS-free ones in terms of output SINR.

This study aimed to develop a deep learning model, capable of extracting both spatial and temporal features from contrast-enhanced ultrasound (CEUS) data and integrating with patient clinical parameters, for the differential diagnosis between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). We retrospectively analyzed the CEUS data (ultrasound contrast agent: SonoVue®—sulfur hexafluoride microbubbles) from 165 ICC patients and 140 date-matched HCC patients. A deep learning model, namely CEUS-CD-Net, was developed to extract spatial–temporal features from dynamic CEUS data and integrate them with patient clinical parameters for the differential diagnosis between HCC and ICC. The performance of CEUS-CD-Net was evaluated using the area under the receiver operating characteristic curve (AUC), with comparisons against other methods including the single-source data-based models (CEUS-Net and CD-Net, based merely on dynamic CEUS or patient clinical data), CEUS static image-based model (sCEUS-Net), time–intensity curve-based model (TIC-Model), and the assessment by radiologists. CEUS-CD-Net achieved an AUC of 0.884 (95

Synthetic aperture radar (SAR) can produce well-focused images based on accurate observation models. However, motion errors in the data acquisition process often introduce inaccuracies in the models and degrade the image quality. Classical motion compensation (MOCO) methods can mitigate this problem, but they are not applicable to compressed sensing (CS) SAR imaging. Existing CS SAR imaging methods can jointly estimate and compensate the motion error from the data by iterative optimization, but they incur a high computational cost. To solve these problems, in this article, we propose an efficient data-driven MOCO strategy for CS SAR imaging. Specifically, we develop a two-step measurement estimation scheme followed by a fitting and filtering procedure to extract the motion error from the data. Then, we use the estimated motion error to correct the CS SAR observation model and reformulate a sparse SAR reconstruction problem based on the corrected model. This strategy significantly reduces the computational cost compared with existing CS SAR MOCO methods. To further expedite the image recovery, we design a fast imaging algorithm that exploits the feature of the observation matrix to accelerate the matrix-vector products and the interpolation operations involved in the recovery problem. Experimental results show that the proposed method can efficiently reconstruct SAR images from CS SAR data with motion errors and offer favorable imaging performance.

This letter investigates the wide-beam power gain pattern synthesis with accurate sidelobe control of phase-only linear arrays by maximizing the minimum power gain of mainlobe region. The resultant problem is greatly intractable due to plentiful nonconvex fractional constraints and unimodular constraints. To address this issue, we convert each fractional constraint into a convex one via iterative convex enhancement and tackle the unimodular constraints by leveraging the penalty convex-concave procedure method. The original problem is then translated into a series of constrained convex subproblems, which are easy to solve. Numerical results illustrate that the proposed method has superior performance of mainlobe power gain to typical shaped beam pattern synthesis methods, and the power gain loss yielded by the phase-only transmitting seems acceptable compared to the corresponding digital array.

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.

This paper equips a reconfigurable intelligent surface (RIS) to assist the active array radar for boosting its interference suppression ability and enhancing the beamforming gain towards target direction simultaneously. The output signal-to-interference-plus-noise ratio (SINR) is chosen as the metric to jointly design the transmit and receive beamformers of radar array and RIS reflection coefficients. In light of SINR performance and implementation complexity, two operation modes using identical or distinct RIS reflection coefficients in transmit and receive stages (ITR or DTR) are investigated. In each mode, the proposed joint design is formulated into a nonconvex constrained fractional programming problem and the solving algorithm is customized under the block coordinate descent framework. Specifically, the RIS reflection coefficients are respectively optimized by the quartic Riemannian Newton method (RNM) in ITR mode and by the quadratic RNM in DTR mode after Dinkelbach transform. Moreover, a simplified scheme under DTR mode is also given to speed up processing, where the beamforming of radar array and RIS separately concentrates on the beamforming gain enhancement and interference suppression in transmit and receive stages. Numerical results display that both ITR and DTR modes significantly outperform the array radars using an RIS only in receive or transmit stage.

Although the Segment Anything Model (SAM) has advanced medical image segmentation, its Bayesian adaptation for uncertainty-aware segmentation remains hindered by three key issues: (1) instability in Bayesian fine-tuning of large pre-trained SAMs; (2) high computation cost due to SAM’s massive parameters; (3) SAM’s black-box design limits interpretability. To overcome these, we propose E-BayesSAM, an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability. T-VBI innovatively reinterprets SAM’s output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation. SO-KAN improves token prediction with learnable spline activations via self-supervised learning, providing insight to prune redundant tokens to boost efficiency and accuracy. Experiments on five ultrasound datasets demonstrated that E-BayesSAM achieves: (i) real-time inference (0.03 s/image), (ii) superior segmentation accuracy (average DSC: Pruned E-BayesSAM’s 89.0 GitHub .

Synthetic Aperture Radar (SAR) images are conventionally visualized as grayscale amplitude representations, which often fail to explicitly reveal interference characteristics caused by external radio emitters and unfocused signals. This paper proposes a novel spatial-spectral chromatic coding method for visual analysis of interference patterns in single-look complex (SLC) SAR imagery. The method first generates a series of spatial-spectral images via spectral subband decomposition that preserve both spatial structures and spectral signatures. These images are subsequently chromatically coded into a color representation using RGB/HSV dual-space coding, using a set of specifically designed color palette. This method intrinsically encodes the spatial-spectral properties of interference into visually discernible patterns, enabling rapid visual interpretation without additional processing. To facilitate physical interpretation, mathematical models are established to theoretically analyze the physical mechanisms of responses to various interference types. Experiments using real datasets demonstrate that the method effectively highlights interference regions and unfocused echo or signal responses (e.g., blurring, ambiguities, and moving target effects), providing analysts with a practical tool for visual interpretation, quality assessment, and data diagnosis in SAR imagery.

In this work, we investigate the gridless parameter estimation of pulse–Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition system integrating a tunable analog component, low-resolution quantizers, and a digital filter. Under the framework of task-based quantization, the HAD architecture is designed to optimize target parameter estimation within the constraints of the bit budget. Specifically, a small subset of the received signal samples is observed and the low-rank parameter matrix is recovered using matrix completion techniques. The atomic norm minimization method is applied to reconstruct the complete parameter matrix, enabling gridless estimation of the parameters. Numerical experiments are conducted to validate the effectiveness of the proposed receiver in gridless parameter estimation.

This article proposes a novel eigenvalue-based detector, called Lambda-1 detector, for adaptive and robust interference detection in single-look-complex (SLC) synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block's covariance matrix, and compares the largest eigenvalue $\lambda _{1}$ with a threshold to determine the presence of interference under the criteria of constant false alarm rate (CFAR), thereby enabling adaptive interference detection against varying levels of interference-to-signal ratios (ISRs). The largest eigenvalue is characterized by the order-2 Tracy-Widom distribution (no closed-form expression) under the assumption of the image's homogeneity, and the threshold is adaptively determined based on a scaled and shifted Gamma distribution that fits this distribution with a closed-form expression. The method is robust by first modeling and then correcting the impacts of upsampling and windowing of SAR image data on the fit distribution's parameters, and by incorporating outlier removal preprocessing. Experimental results validate the effectiveness of the proposed method in successfully detecting both strong and weak interferences in various SAR images, including Sentinel-1 and Gaofen-3. The detection performance is quantitatively evaluated using false alarm rate $P_{\mathrm { fa}}$ and detection rate $P_{d}$ . In summary, the proposed Lambda-1 detector effectively identifies interference artifacts in focused SAR images and holds promise for improving the quality of SAR imagery by incorporating adaptive interference removal.

The positioning accuracy of global navigation satellite system (GNSS) in urban areas can be significantly affected by non-line-of-sight (NLOS) receptions. Vector tracking loop (VTL) which utilizes information from multiple satellites has been proposed for mitigating the NLOS detection. This paper develops a robust adaptive fading unscented Kalman filter (AF-UKF-) based navigation filter for NLOS mitigation in VTL-based systems. It integrates the AF-KF approach previously proposed for adaptive Bayesian KF to the UKF-based navigation filter to update the scale of the state and measurement noise covariances so that system noise and measurement uncertainties can be mitigated. A robust extension of the AF-UKF algorithm based on robust statistics is proposed to effectively detect and correct NLOS reception in each KF update. A robust initialization method is also proposed to provide a reliable warm start to the robust AF-UKF so that the latter can reliably start-up even under NLOS reception and limited initial position information. The proposed methodwas evaluated and compared with conventional algorithms using real GPS signal datasets and simulated data. Results showed that the proposed navigation solution yields more accurate and reliable positioning performance than conventional methods under NLOS reception. The advantage and stability of the AF-KF and AF-UKF are also analyzed.

Compressed sensing (CS) synthetic aperture radar (SAR) can recover images from undersampled SAR data based on accurate observation models. However, motion errors often cause inaccuracies in observation data and result in defocusing of the reconstructed SAR images. Existing methods can restore and compensate the motion error from data by iterative optimization, which, however, leads to significantly increased computational cost. In this article, we propose an efficient motion compensation (MOCO) scheme for CS SAR using measured antenna phase center (APC) data. Specifically, we exploit the motion error measured by the navigation device to correct the CS SAR observation model. Then, we use the corrected model to formulate a new sparse SAR reconstruction problem. This leads to substantially lower computational cost than the existing MOCO methods in CS SAR. To further achieve fast image recovery, we design a fast imaging algorithm for CS SAR with MOCO to speed up some matrix-vector products involved in the reconstruction problem. The experimental results demonstrate that the proposed method can efficiently reconstruct SAR images from CS SAR data with motion errors.