Hyperspectral image super-resolution (HSI-SR) is fundamentally more difficult than RGB image SR, since its ultrahigh spectral dimensionality. Existing supervised methods rely on labeled training data to obtain data prior, which incurs prohibitive collection costs and limits generalization. Unsupervised methods individually preset the band and kernel with handcrafted priors, whereas this decoupling modeling artificially creates a complexity-performance trade-off in the selected band number. To address these issues, we propose BKX-HMM, a unified statistical framework for blind HSI-SR, which uniformly models the band selection, kernel estimation, and HSI restoration through the state transition of a hidden Markov model (HMM). BKX-HMM redefines the trade-off as a distributional fitting problem: each Markov transition progressively learns optimal parameters of full-band distribution via limited spectral observations. Based on BKX-HMM, we propose BKSR, the first unsupervised blind HSI-SR method, which consists of three synergistic modules: Gibbs sampling-based band selection (GBS), test-time-training kernel estimation (TKE), and robust HSI restoration (RHR). These modules form a closed-loop optimization cycle: i) In GBS, the dynamic ergodicity of Gibbs sampling provides a global spectral view for kernel estimation and HSI restoration while maintaining local spectral computations; ii) In TKE, the GBS-sampled bands guide the kernel estimator update, achieving a learnable sampling-based mechanism, which refines kernel estimation to regularize RHR's diffusion trajectory; iii) In RHR, a spectral hyper-Laplacian prior is integrated into the reverse process of an off-the-shelf diffusion model, which achieves non-i.i.d. noise robust HSI restoration, feedback reweights band and kernel importance for subsequent GBS and TKE iterations. Extensive experiments on both synthetic and real HSI datasets demonstrate our BKSR's superiority over baseline methods across diverse scenarios (e.g., unknown Gaussian/motion kernel, non-i.i.d. noise) while maintaining comparable computational costs to the classic band selection methods.

Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments. This paper addresses the joint optimization of constant modulus waveforms to enhance both target detection and parameter estimation concurrently. A unified design framework is developed by maximizing a mutual information upper bound (MIUB), which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting. Realistic, potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions (GMDs), enabling tractable closed-form approximations of the MIUB’s Kullback–Leibler divergence and mutual information components. To tackle the ensuing non-convex optimization, a tailored metaheuristic phase-coded dream optimization algorithm (PC-DOA) is proposed, incorporating hybrid initialization and adaptive exploration–exploitation mechanisms for efficient phase-space search. Numerical results substantiate the proposed approach’s superiority in achieving favorable detection estimation trade-offs over existing benchmarks.

Instead of maximizing the detection probability, an information-theoretic waveform design method using Cauchy-Schwarz Divergence (CSD) between the probability density functions of the two hypotheses (viz., the target is present/absent) is proposed for improving the target detection performance. A Minorization-Maximization (MM) approach is introduced to deal with the non-convex waveform optimization problem under the energy constraint. Numerical results demonstrate the feasibility and effectiveness of the proposed algorithm.

Clutter suppression remains a fundamental challenge in multi-channel SAR moving target detection. Among various approaches, Space-Time Adaptive Processing (STAP) has been widely employed due to its excellent clutter suppression capabilities. However, STAP intrinsically assumes complex Gaussian clutter, which often significantly deviates from actual heterogeneous clutter environments encountered in practical scenarios. Such deviations can markedly impair the clutter suppression performance of STAP. To overcome this issue, this paper proposes a Kullback-Leibler Divergence (KLD)-based Hermitian Positive Definite (HPD) manifold Dimensionality Reduction (DR) technique, aimed at enhancing the feature discrimination of moving target on HPD manifold. Initially, the airborne multi-channel SAR image domain data are mapped into the HPD manifold to form a feature manifold. Next, an optimal dimensionality reduction mapping is constructed by formulating an optimization problem on the HPD manifold that maximizes the KLD, and an analytical solution to this problem is derived. Based on this solution, an optimal reduction matrix is designed, projecting the data into a low-dimensional HPD manifold. Finally, on the reduced HPD manifold, a Matrix Information Geometry (MIG) detector based on KLD is employed to achieve enhanced detection of moving targets. The proposed multi-channel SAR moving target detection framework incorporating KLD-based dimensionality reduction does not rely on specific parametric statistical model of the clutter background, only utilizes geometric properties derived from second-order statistic. This effectively addresses the performance degradation caused by statistical distribution model mismatch in complex environments. Moreover, deriving an analytical solution to the optimization problem avoids the high computational costs associated with Riemannian conjugate gradient algorithms, thus reducing complexity substantially and facilitating practical implementation in SAR systems. Experimental results, including simulations and measurements with real clutter backgrounds, demonstrate the superior effectiveness of the proposed approach.

Heterogeneous clutter suppression is an important challenge for multi-channel synthetic aperture radar (SAR) moving target detection. The knowledge-aided space time adaptive processing (STAP) method is widely used due to its superior clutter suppression performance. In this process, it is necessary to use independent and identically distributed (IID) training data to estimate the clutter covariance matrix (CCM) of the cell under test (CUT). However, heterogeneous environments such as clutter power fluctuations and moving target contamination in training samples will significantly reduce the estimation accuracy of CCM, which is not conducive to subsequent clutter suppression processing. To solve this problem, this paper introduces the covariance matrix estimation method based on the geometric mean of Hermitian positive-definite (HPD) manifold into the multi-channel SAR moving target detection. Firstly, the airborne multi-channel SAR echo is characterized on the HPD manifold, which better reveals the nonlinear geometric structure of the multi-channel SAR echo data than in Euclidean space. Based on the echo characterization on the HPD manifold, the CCM estimation problem is transformed into the geometric mean estimation problem on the HPD manifold. At the same time, the generalized inner product (GIP) criterion is used in training sample selection to reduce the negative impact of contaminated samples. Finally, the CCM estimation based on geometric mean is applied to the knowledge-aided SAR-STAP, which effectively improves the clutter suppression performance. Given the large volume of multi-channel SAR echo data, this paper comprehensively evaluates algorithm performance and computational complexity, and selects the geometric metric criterion for multi-channel SAR moving target detection. Based on this criterion, a multi-channel SAR moving target detection framework driven by HPD manifold is proposed. The detection performance of the proposed framework is verified on simulated and measured clutter backgrounds.

Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pretraining on labelled datasets. This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pretraining-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation, and thereby enables superior HR image restoration performances. This is achieved by a Markov chain Monte Carlo sampling process on random kernel distributions. The learned kernel prior is then assigned to optimize a blur kernel estimation network, which entails a network-based Langevin dynamic optimization strategy. These two techniques ensure the accuracy of the kernel estimation. DKP can be easily used to replace the kernel estimation models in the existing methods, such as Double-DIP and FKP-DIP, or be added to the off-the-shelf image restoration model, such as diffusion model. In this paper, we incorporate our DKP model with DIP and diffusion model, referring to DIP-DKP and Diff-DKP, for validations. Extensive simulations on Gaussian and motion kernel scenarios demonstrate that the proposed DKP model can significantly improve the kernel estimation with comparable runtime and memory usage, leading to state-of-the-art BSR results. The code is available at https://github.com/XYLGroup/DKP.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when compared with the Start-of-the-Art solutions on synthesis and real-world datasets.

Physical parameter estimation of satellite is crucial in space situation awareness as it reflects valuable information. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is a powerful sensor for space surveillance, providing rich information for satellite physical parameter estimation. Parabolic antennas, which are widely loaded in remote sensing and communication satellites, have received great attention recently. Dedicated to the space situation awareness issue using Pol-ISAR, a physical parameter joint estimation method of satellite parabolic antenna with key frame Pol-ISAR images is developed in this work. The core idea is to utilize the mapping relationship between parabolic antenna in 3-D space and its projection ellipse in 2-D ISAR image. Under special observation geometry, the closed-form expressions of parabolic antenna physical parameters are deduced for the first time, providing an efficient way for parameter estimation. Moreover, the abundant information within Pol-ISAR images is mined and utilized. Various polarimetric features are adopted for ellipse extraction and the subsequent physical parameter estimation. Compared with single-polarization channel data, the superiorities of polarimetric feature are validated using electromagnetic simulation data.

Video synthetic aperture radar (ViSAR) can produce continuous images with a high frame rate and contain the moving target’s shadow, which provides numerous advantages for detecting moving targets. In this letter, a novel moving target detection method based on convolutional neural network (CNN) is proposed. The proposed network has a 3D convolutional encoding path, a 2D convolutional decoding path, and a bridge path to efficiently capture the target’s shadow information and summarize the spatiotemporal features from raw continuous images to high-level semantics. Furthermore, based on coordinate attention (CA), a temporal tri-coordinate attention (TTCA) module is proposed to obtain key spatiotemporal features in ViSAR data. The validity of the proposed method has been confirmed through experiments with actual ViSAR data and shows superior performance in the suppression of false alarms and missing alarms.

Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linear structure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linear structure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.

Recent literature shows that with sufficient cyclic prefix, orthogonal frequency-division multiplexing signals can convert a synthetic aperture radar (SAR) image with interrange cell interference (IRCI) to an IRCI-free SAR image. In this letter, the imaging algorithm is carefully investigated and it is shown that the range reconstruction accuracy can be further improved. By reformulating the system model in a linear model form, the minimum variance unbiased estimator of the range profile is derived, which can attain the Cramer-Rao lower bound, making our algorithm more effective than current algorithms under noisy conditions. Simulation results validate the advantages of the proposed algorithm, especially under low signal-to-noise ratio conditions.

This letter investigates the micromotion target detection problem for the multichannel synthetic aperture radar (SAR)- ground moving target indication system. The multichannel SAR signal models of the micromotion target and the ground clutter in the raw data domain are established firstly. Then the generalized likelihood ratio test (GLRT) of the micromotion target is derived. Based on the analysis of the probability density functions of the test statistics, theoretical detection performance dependent on the micromotion parameters is provided. Simulated heterogeneous SAR data validate the effectiveness of the GLRT detector.

Micro‐range (m‐R) signatures which are induced by micro‐motion dynamics can be observed from range profiles, providing that the range resolution of radar is high enough. For real scenarios, micro‐motion is often mixed with macro‐motion (translation). To extract the micro‐motion signatures, it is required to remove the macro‐motion component. The widely employed range alignment technique fails for rigid‐body targets with micro‐motion, since the relative distances between different scattering centres on a rigid‐body target are varying and it is unable to obtain a stable reference range profile. Thus, the extracted m‐R signatures will be accompanied with residual macro‐motion, which may lead to the degradation. However, this issue is often ignored in the research of m‐R signatures extraction. In this work, by modelling the motions of scattering centres as the superimposition of a polynomial signal (represents macro‐motion) and a sinusoidal signal (represents micro‐motion), a micro‐motion period estimation method based on high‐order difference sequence is proposed. The property that the difference operation can decrease the order of polynomial signals while preserve sinusoidal signals with the same frequency enables the proposed method to extract m‐R signatures in the presence of macro‐motion. The effectiveness of the proposed method is validated by synthetic and measured radar data.

To maximize the utilization of the available networks-on-chip (NoCs) link bandwidth, partially faulty links with low fault level should be utilized while heavily defected (HD) links should be deactivated and dealt with by means of a fault tolerant routing algorithm. To reach this target, we make the following contributions in this paper: 1) we propose a flit serialization (FS) method to efficiently utilize partially faulty links. The FS approach divides the links into a number of equal width sections, and serializes sections of adjacent flits to transmit them on all fault-free link sections to mitigate the unbalance between the flit size and the actual link bandwidth; 2) we propose the link augmentation with one redundant section as a low cost mechanism to mitigate the FS drawback that a link's available bandwidth is reduced even if it contains only one faulty wire; and 3) we deactivate HD links when their fault level exceed a certain threshold to diminish congestion caused by HD links. The optimal threshold is derived by comparing the zero load packet transmission latency on the HD links and that on the shortest alternative path. Our proposal is evaluated with synthetic traffic and PARSEC benchmarks. Experimental results indicate that the FS method can achieve lower area*power/saturation_throughput value than all state of the art link fault tolerant strategies. With a redundant section in each link, the NoC saturation throughput can be largely improved than just utilizing FS, e.g., 18% when 10% of the NoC wires are broken. Simulation results we obtained at various wire broken rate configurations indicate that we achieve the highest saturation throughput if 4- or 8-section links with a flit transmission latency longer than four cycles are deactivated.

Multiple input multiple output (MIMO) synthetic aperture radar (SAR) shows much potential compared with traditional SAR in many interesting applications, but effective orthogonal waveforms design is still a challenge. Sub-chirp combination waveform has large time-bandwidth product, low cross-correlation, and low peak-average ratio, and it has been used in some MIMO SAR research. However, only the reduction of peak-average ratio is considered in the existing sub-chirp combination waveform optimal design research. In this paper, imaging performance is considered for waveform design, using cost function based on MIMO SAR general ambiguity function (GAF). Furthermore, genetic algorithm is used to obtain optimal waveforms. Finally, the optimal waveforms by the proposed method are compared with the waveform examples in the existing literature, and the simulation results show the effectivity of the proposed optimal design method.

Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class‐dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two‐dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.

This study considers the problem of jointly detecting whether a target is present in a scene and estimating its state, if it is there. This joint detection and estimation problem can be solved using a special case of the multi-target Bayes filter (referred to as the joint target detection and tracking (JoTT) filter). However, if the model used by the JoTT filter does not match the actual dynamics, the filter will tend to miss-detection directly or diverge such that the actual errors fall outside the range predicted by the filter's estimate of the error covariance. A similar difficulty arises, if the target behaviour can switch between different modes of operation, since the filter may then be accurate for only one particular mode. This study proposes a novel joint detection and tracking filter, which is the multiple model extension of the JoTT filter to accommodate the possible target manoeuvring behaviour. In addition, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are proposed. The simulation results are presented to show the effectiveness of the proposed filter over the original JoTT filter.

Targets with uniform rotational motion may cause migration through cross-range cells during the imaging time, which makes the inverse synthetic aperture radar image smeared. Traditional motion compensation methods hardly work well because it is difficult to estimate the quadratic phase error (QPE) caused by uniform rotational motion efficiently. To solve this problem, a novel QPE estimation method based on phase cancellation (PC) is proposed in this letter. In this method, PC is used to eliminate the linear term of the phase, which is a disadvantage to the QPE estimation. By employing the weighted linear least squares algorithm, the QPE can be estimated efficiently and robustly. Experiments with both simulated and measured radar data demonstrate the performance of the proposed method.