Search Results (1,078)

The large number of UAVs under supervision at low altitudes have brought serious security risks to the field of air defense. Accurately analyzing the characteristics of UAVs’ echo signals is of great research significance for the detection and recognition of UAVs. Based on the principle of radar detection, the echo spatial correlation in the distributed radar detection mode is studied. According to the influence of different movement speeds on the micro-motion characteristics of UAVs, the echo signal models of UAVs in two flight states are established. Combined with the instantaneous micro-Doppler frequency model of the ideal motion state of UAVs, micro-Doppler frequency calculation functions of UAVs at different attitude angles are constructed. Through simulation calculation, the variation curves between the observation angle and the echo spatial correlation using different detection distances are given. Based on time–frequency images of UAVs in their ideal motion state, changes in the time–frequency images at different motion speeds and attitude angles are analyzed. These research results will help radar detection systems to accurately recognize UAVs in an uncertain motion state and can also provide a basis for predicting the next motion action of UAVs in subsequent target tracking. Full article

This study proposes an automatic denatured recognition method of biological tissue during high-intensity focused ultrasound (HIFU) therapy. The technique integrates ultrasonic phase space reconstruction (PSR) with a convolutional block attention mechanism-enhanced EfficientNet-B0 model (CBAM-EfficientNet-B0). Ultrasonic echo signals are first transformed into high-dimensional phase space reconstruction trajectory diagrams using PSR, which reveal distinct fractal and chaotic characteristics to analyze tissue complexity. The CBAM module is incorporated into EfficientNet-B0 to enhance feature extraction from these nonlinear dynamic representations by focusing on critical channels and spatial regions. The network is further optimized with Dropout and Scaled Exponential Linear Units (SeLUs) to prevent overfitting, alongside a cosine annealing learning rate scheduler. Experimental results demonstrate the superior performance of the proposed CBAM-EfficientNet-B0 model, achieving a high recognition accuracy of 99.57% and outperforming five benchmark CNN models (EfficientNet-B0, ResNet101, DenseNet201, ResNet18, and VGG16). The method avoids the subjectivity and uncertainty inherent in traditional manual feature extraction, enabling effective identification of HIFU-induced tissue denaturation. This work confirms the significant potential of combining nonlinear dynamics, fractal analysis, and deep learning for accurate, real-time monitoring in HIFU therapy. Full article

This study proposes a joint denoising method based on intelligent optimization variational mode decomposition (VMD) and normalized least mean square error (NLMS). Experiments show that this method has good adaptability to non-stationary weak signals (such as medical ultrasonic Doppler signals), effectively separating signal components through VMD’s multi-scale decomposition and combining with NLMS’s adaptive filtering mechanism to suppress local noise. However, in scenarios with strong transient interference (such as mechanical vibration noise), the deviation in modal number selection of VMD leads to a decrease in decomposition efficiency; under low sampling rate conditions (<20 kHz), the steady-state convergence speed of NLMS is reduced by approximately 35%. Therefore, the universality of this method in complex noise environments requires further verification. This study provides a new theoretical perspective for non-stationary signal processing, but parameter optimization needs to be combined with specific noise characteristics in practical engineering applications. Full article

The complexity of land types and the limited spatial resolution of Synthetic Aperture Radar (SAR) imagery have led to widespread mixed-pixel contamination in radar backscatter images. The radar backscatter echo signals from a mixed pixel are often a combination of backscattering contributions from multiple endmembers. The signal mixture of endmembers within mixed pixels hinders the establishment of accurate relationships between pure endmembers’ parameters and the corresponding backscatter coefficient, thereby significantly reducing the accuracy of surface parameter inversion. However, few studies have focused on decomposing and estimating the pure backscatter signals within mixed pixels. This paper proposes a novel approach based on hyperspectral unmixing techniques and the microwave backscatter contribution decomposition (MBCD) model to estimate the pure backscatter coefficients of all Endmembers within mixed pixels. Experimental results demonstrate that the model performance varied significantly with endmember abundance. Specifically, high accuracy was achieved in estimating soil backscattering coefficients when vegetation coverage was below 25% ($R^2\approx 0.88$, with 98% of pixels showing relative errors within 0–20%); however, this accuracy declined as vegetation coverage increased. For grass endmembers, the model maintained high estimation precision across the entire grassland area (vegetation coverage 0.2–0.8), yielding an of 0.80 with 83% of pixels falling within the 0–20% relative error range. In addition, the model performance is influenced by the number of endmembers. Full article

Detecting weak echoes from low-RCS targets in pulsed radar systems presents significant challenges, as conventional coherent accumulation methods require extended dwell times that reduce data rates and suffer from target-motion-induced migration. We propose RD-Transformer, an end-to-end attention-based architecture that reformulates coherent integration as a learned feature fusion problem. The framework integrates multi-pulse transpose preprocessing, dual-path self-attention encoders for transmitted and received signals, and a cross-attention decoder to extract transmit-receive interaction features. A tunable sigmoid-based gating mechanism enables flexible false alarm control during inference. Experiments on synthetic pulsed-radar data demonstrate that, under identical false alarm constraints (P_fa = 1 × 10⁻² to 1 × 10⁻⁵) and using only 10 coherent pulses, RD-Transformer reduces the required SNR by 14–20 dB compared to optimal energy detection across Swerling I-IV target fluctuation models, validating the effectiveness of learned coherent accumulation for weak target detection. Full article

The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in a single observational channel, while feature space methods are overly sensitive to the number of pulse accumulations and rigidly apply outlier classifiers to define detection regions, without theoretical derivation. To address these limitations, this paper proposes a two-dimensional (2D) CFAR target detection method based on echo data from dual-polarization observational channels. First, statistical models of the amplitude distribution for horizontal–horizontal (HH) and vertical–vertical (VV) polarization sea clutter radar echoes are validated under identical observation conditions using measured data, and their correlations are analyzed. Then, the Copula function is introduced as a theoretical foundation to rigorously derive and extend the cell-averaging CFAR detector through strict mathematical formulations, transitioning from single statistics to 2D detection statistics. This leads to the proposed target detection method. Testing with measured data from publicly available datasets demonstrates that the proposed method effectively achieves adaptive false alarm control and significantly improves the detection performance compared to existing single-pulse one-dimensional CFAR detection methods. Full article

Tidal flats play a vital role in coastal ecosystems by supporting biodiversity, mitigating natural hazards, and functioning as blue carbon reservoirs. However, monitoring their geomorphological changes remains challenging due to high turbidity, shallow depths, and tidal variability. Conventional approaches—such as satellite remote sensing, acoustic sounding, and topographic LiDAR—face limitations in resolution, accessibility, or coverage of submerged areas. Airborne bathymetric LiDAR (ABL), which uses green laser pulses to detect reflections from both the water surface and seabed, has emerged as a promising alternative. Unlike traditional discrete-return data, full waveform analysis offers greater accuracy, resolution, and reliability, enabling more flexible point cloud generation and extraction of additional signal parameters. A critical step in ABL processing is waveform decomposition, which separates complex returns into individual components. Conventional methods typically assume fixed models with three returns (water surface, water column, bottom), which perform adequately in clear waters but deteriorate under shallow and turbid conditions. To address these limitations, we propose an adaptive progressive Gaussian decomposition (APGD) tailored to tidal flat environments. APGD introduces adaptive signal range selection and termination criteria to suppress noise, better accommodate asymmetric echoes, and incorporates a water-layer classification module. Validation with datasets from Korea’s west coast tidal flats acquired by the Seahawk ABL system demonstrates that APGD outperforms both the vendor software and the conventional PGD, yielding higher reliability in bottom detection and improved bathymetric completeness. At the two test sites with different turbidity conditions, APGD achieved seabed coverage ratios of 66.7–70.4% and bottom-classification accuracies of 97.3% and 96.7%. Depth accuracy assessments further confirmed that APGD reduced mean depth errors compared with PGD, effectively minimizing systematic bias in bathymetric estimation. These results demonstrate APGD as a practical and effective tool for enhancing tidal flat monitoring and management. Full article

We present experimental results related to the features and geophysical conditions for the occurrence of the long-delay echo (LDE) signals in the medium-wave (MW) frequency range observed on 20 January 2025, at the Gor’kovskaya observatory near St. Petersburg (60.27° N, 29.38° E). A total of 19 series of experiments on guiding MF in the magnetosphere were carried out, while LDE signals were only registered on January 20, 2025, in evening hours, when the most disturbed conditions were observed (Kp = 4+, ΣKp = 27−). It was found that the LDE signals, with delay times of 310–322 ms, were observed in the evening hours under disturbed magnetic conditions. In such a case, the MW propagates into the magnetosphere to the magnetically conjugate point, is reflected from the topside ionosphere, and returns. The frequency of sounding signal f_SS exceeded the critical frequency of the F2 layer at Gor’kovskaya observatory foF2_GRK but was less than the critical frequency at the magnetic conjugated point foF2_MCP, foF2_GRK < f_SS < foF2_MCP. The LDE signals were observed in the narrow frequency range from 2100 to 2400 kHz. The background geophysical conditions during the occurrence of LDE signals were analyzed using the CADI ionosonde data and Swarm satellite observations. The plausible generation mechanisms for MW guiding in the magnetosphere are discussed. Full article

Based on the WRF-3DVar system, this study investigates the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case that occurred over complex terrain in China on 30 May 2024. It is found that radar data assimilation with spatial truncation significantly enhances the representation of convective structures while reducing false echoes by about 40%. However, when the variance and correlation length scales are enlarged, reflectivity intensity is increased by 5–10 dBZ with false signals and positional errors also introduced, while a balanced scheme is observed to yield the highest skill scores. Assimilation of AWS alone provides limited improvements, whereas radar assimilation introduces localized structures that rapidly decay within 1–2 h due to the absence of boundary-layer constraints. The benefits of joint assimilation are clearly demonstrated in terms of spatial continuity and vertical consistency, with the assimilation order being identified as a decisive factor. When AWS is assimilated prior to radar, low-level thermodynamic and dynamic conditions are optimized, leading to strengthened cold pool structures by around 2 K, enhanced updrafts by over 20%, and improved wind distribution. The critical role of AWS-radar joint assimilation in capturing the dynamical characteristics of squall lines is thus highlighted. Detailed examination of the forecast and analysis indicates that assimilating AWS before radar not only optimizes boundary-layer conditions but also enhances the coupling between cold pools and updrafts, resulting in improved simulation accuracy in both horizontal and vertical structures. These findings provide valuable insights for advancing the prediction of severe convective systems. Full article

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

Cerebral ischemia represents a major mortality and disability cause; oxidative stress is the main intensifier mechanism of excitotoxicity, neuroinflammation, blood–brain barrier failure, and neuronal loss; under these circumstances, firm, mechanism-anchored neuroprotection is an absolute necessity. The work includes a exhaustive, PRISMA (Preferred reporting items for systematic review and meta-analysis)-adherent presentation of the effects of antioxidant peptides and small molecules on tissues, unifying disparate readouts into a coherent tissue-level narrative. A systematic interrogation was performed across major databases over a prespecified interval, applying transparent eligibility criteria to studies that quantified canonical endpoints—infarct volume, neuronal integrity (NeuN/MAP2), apoptosis (TUNEL/cleaved caspase-3), gliosis (GFAP/Iba1), and ultrastructural preservation. The evidence coalesces around a strikingly consistent signal: antioxidant strategies converge on smaller infarcts, robust preservation of neuronal markers, attenuation of apoptotic burden, dampened astroglial–microglial reactivity, and stabilization of mitochondrial and axonal architecture—patterns that align with antioxidative, anti-apoptotic, anti-inflammatory, and ferroptosis-modulating mechanisms. While early clinical data echo these benefits, translation is tempered by heterogeneity in models, timing and dosing windows, and outcome batteries. By consolidating the histological landscape and pinpointing where effects are durable versus contingent, this work elevates antioxidant peptide and small-molecule neuroprotection from promising fragments to an integrated framework and sets an actionable agenda—standardized histological endpoints, protocol harmonization, head-to-head comparisons of peptide versus small-molecule strategies, and adequately powered randomized trials embedded with mechanistic biomarkers to decisively test efficacy and accelerate clinical adoption. Full article

To accurately simulate the progression of pipeline corrosion, this paper proposes a three-dimensional corrosion modeling method for curved random surfaces based on spatial frequency composition. It applies this method to the inner surface of layered pipelines to emulate both the morphological characteristics and the evolution of internal corrosion. Combined with ultrasonic guided wave technology, the approach enables quantitative assessment of internal corrosion in layered pipelines. First, trigonometric series expansion and nonlinear polynomial superposition are used to characterize the roughness and curvature of the corroded surface, respectively, establishing a mathematical model capable of accurately representing complex corrosion morphologies. Next, a COMSOL–ABAQUS co-modeling approach is employed to build a finite element model of a three-layer composite pipeline consisting of a steel pipe, an insulating layer, and an anti-corrosion layer, with curved random-surface corrosion on the inner surface of the steel pipe. Finally, a wavelet packet decomposition algorithm is applied to extract features from the guided wave echo signals, creating a damage index matrix to correlate the corrosion area with the damage index quantitatively. The results show that the damage index increases steadily with the corrosion area, confirming the effectiveness of the proposed method. This study provides an alternative technical approach for high-fidelity modeling and precise assessment of pipeline corrosion detection. Full article

Underwater laser range-gated imaging (ULRGI) effectively suppresses backscatter from water bodies through a time-gated photon capture mechanism, significantly extending underwater detection ranges compared to conventional imaging techniques. However, as imaging distance increases, rapid laser power attenuation causes localized pixel loss in captured images. To address ULRGI’s limitations in multi-frame stacking—particularly poor real-time performance and artifact generation—this paper proposes the Photon-Level Physically Guided Underwater Laser-Gated Image Restoration Network (PLPGR-Net). To overcome image degradation caused by water scattering and address the challenge of strong coupling between target echo signals and scattering noise, we designed a three-branch architecture driven by photon-level physical priors. This architecture comprises: scattering background suppression module, sparse photon perception module, and enhanced U-Net high-frequency information recovery module. By establishing a multidimensional physical constraint loss system, we guide image reconstruction across three dimensions—pixels, features, and physical laws—ensuring the restored results align with underwater photon distribution characteristics. This approach significantly enhances operational efficiency in critical applications such as underwater infrastructure inspection and cultural relic detection. Comparative experiments using proprietary datasets and state-of-the-art denoising and underwater image restoration algorithms validate the method’s outstanding performance in deeply integrating physical interpretability with deep learning generalization capabilities. Full article

The Martian North Polar Layered Deposits (NPLD) are a thick sequence of ice and dust layers that serve as a primary archive of the planet’s recent climate history. The Shallow radar (SHARAD) sounder provides critical data for probing this stratigraphy, but its resolution is limited by the radar wavelet and the signal-to-noise ratio (SNR). Deconvolution aims to overcome the resolution limitation, with recent techniques like sparse deterministic deconvolution (SDD) showing promise. However, the performance of these methods is fundamentally constrained by noise, which can obscure weak reflectors from deep or subtle layers and introduce artifacts into the results. This study proposes a two-stage workflow that directly addresses the noise problem by applying structure enhancement denoising (SED) to improve the SNR and reflector echo power of SHARAD radargrams prior to deconvolution, thereby enhancing the overall fidelity of the results. The efficacy of this integrated SED-SDD workflow is validated first on synthetic radargrams, demonstrating a marked reduction in reflectivity error and superior preservation of structural detail compared to the baseline SDD method, especially under low-SNR conditions. Subsequently, the workflow is applied to a real SHARAD observation of the NPLD, revealing enhanced lateral continuity of subtle reflectors and a significant reduction in noise-induced artifacts. The results demonstrate that this synergistic approach provides a powerful tool for extracting higher-resolution stratigraphic information from noisy planetary orbital radar data, thereby advancing our ability to interpret the sedimentary history of Mars. Full article

With respect to distributed MIMO radar systems, conventional far-field detection methods fail under near-field conditions due to significant wavefront curvature, which inevitably results in target energy loss and erroneous parameter estimation. To solve this problem, we propose a near-field target detection framework based on range–angle-coupled matching in this study. Firstly, we design the linear frequency modulation by frequency division (FD-LFM) signal. In addition to offering favorable orthogonality and Doppler tolerance, the transmitter of distributed MIMO radar employs a wide beamwidth to mitigate the low scanning efficiency associated with beam positioning in distributed phased array (PA) radar systems. Secondly, we develop a three-dimensional grid-based echo model for near-field targets in range–azimuth–elevation domain. Specifically, we conceive a coherent pulse integration method via multi-dimensional matching, which enables precise delay alignment and echo accumulation across all transmit–receive pairs for accurate near-field target detection. Thirdly, we propose a parallelization scheme for distributed MIMO radar near-field processing. Our proposal not only compensates effectively for spherical wave propagation effects but also achieves real-time processing through GPU acceleration. Finally, our proposed method’s feasibility of high resolution and effectiveness of near-field detection have been verified by field experimental simulation and actual measurement processing results. Full article