Search Results (169)

Automatic carrier landing of fixed-wing UAVs remains challenging under deck motion, carrier airwake, gusts, and actuator faults. This paper proposes a robust flexible predefined-time prescribed performance control (RFPTPPC) framework with beneficial disturbance utilization (BDU). A control-oriented six-degree-of-freedom cascaded model with direct lift control is first established. To address the temporary infeasibility of fixed predefined-time PPC boundaries, direction-selective flexible boundaries restore the admissible attitude error region when the nominal envelope is threatened, while a smoothly coordinated recovery branch drives the error inward. An adaptive super-twisting extended state observer (ASTESO) reconstructs lumped disturbances in the cascaded loops. Using the ASTESO outputs, BDU evaluates each disturbance component, retains those that favor error convergence, and compensates for adverse components, thereby improving attitude tracking accuracy and maintaining PPC feasibility. Lyapunov analysis establishes practical predefined-time stability of the closed-loop system. Comparative simulations demonstrate improved trajectory tracking, attitude regulation, actuator coordination, and robustness under randomized landing conditions. Full article

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers. Full article

TiAl alloy exhibits excellent strength, oxidation resistance and creep resistance, making it a preferred candidate material to replace high-temperature alloys. Currently, TiAl alloy has been widely applied in aerospace, the marine industry and other fields involving high-stress contact or highly corrosive environments. Selective laser melting (SLM) technology provides a brand-new approach for the fabrication of TiAl alloys, which enables direct forming of workpieces with complex structures and significantly reduces manufacturing cycles. However, the quality and performance of SLM fabricated TiAl alloys are highly dependent on laser energy input. Therefore, this study fabricated TiAl4822 alloy under different SLM process parameters, and systematically conducted investigations on its tribological properties and electrochemical corrosion behavior. The experimental results show that the SLM process did not alter the basic phase composition of TiAl4822 alloy, with Ti_0.6Al_0.4 as the dominant phase. TiAl4822 alloys fabricated under the parameter combinations of 1000 mm/s + 140 W exhibited outstanding wear resistance, and the wear mechanism transformed from severe adhesion and abrasive wear to mild oxidative wear. When the laser power was 100 W and the scanning speed was 1200 mm/s, the alloy achieved the highest corrosion resistance, with the corrosion potential reaching the maximum value of −390.065 mV and the corrosion current density decreasing to the minimum value of 8.73 × 10⁻⁶ A/cm². Thus, different parameter combinations can realize the optimization of tribological properties and electrochemical corrosion performance respectively. This study lays a theoretical foundation for promoting the high-performance engineering application of this alloy in harsh wear-resistant and corrosion-resistant environments. Full article

Carrier-based fixed-wing UAV landing is challenged by deck motion, carrier airwake, gust disturbances, strong trajectory–attitude coupling, and actuator constraints. To address these issues, this paper proposes a Predefined-Time Direct Lift/Side-Force-Integrated Approach Landing (PTDIAL) method. An integrated direct-force architecture is constructed using the trailing-edge flap for direct lift and the spoiler for direct side force, thereby reducing the dependence of trajectory correction on angle-of-attack- and bank-angle/sideslip-mediated regulation. A preview-based reference glide slope is generated from the predicted Ideal Touch Point (ITP) sequence to improve the response to deck motion. Predefined-time control laws are developed for the cascaded position, trajectory, attitude, angular rate, and velocity loops, with prescribed-performance constraints imposed on the attitude response. A predefined-time disturbance observer is introduced to estimate the lumped aerodynamic disturbances, while an auxiliary anti-saturation mechanism compensates for the effect of trailing-edge flap saturation. Lyapunov analysis establishes the practical predefined-time stability of the closed-loop system under bounded disturbances and actuator constraints. Various simulations demonstrate that the proposed architecture improves lateral and vertical tracking while preserving the UAV attitude, and Monte Carlo simulations further confirm the robustness of PTDIAL. Full article

Detecting ship critical components in UAV-based maritime monitoring remains challenging because of small-object detail loss, multi-scale semantic misalignment, and limited computational resources on embedded platforms. To address these issues, this study proposes D-MobileNetV3, a lightweight anchor-free detection algorithm based on an enhanced CenterNet framework. In the proposed framework, MobileNetV3 is used as the lightweight backbone, and an RFB module is introduced to improve multi-scale contextual representation. A High-Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with a serially coupled dual-path architecture is then constructed by integrating the High-Frequency Perception (HFP) and the spatial dependency perception module (SDP), enabling edge-detail enhancement and spatial-semantic alignment during multi-scale fusion. In addition, a lightweight decoupled detection head with SIoU loss is designed to improve the localization of slender components such as waterlines and masts. Experiments on the in-house VitalShips dataset and the public SeaShips dataset show that D-MobileNetV3 improves detection accuracy and generalization relative to the baseline models while maintaining low computational cost. UAV flight tests on the Jetson AGX Xavier platform achieve an average processing time of 16.3 ms and an mAP50 of 83.5%, indicating that the proposed method provides a high-precision, low-latency solution for maritime surveillance. Full article

Complex flight maneuver recognition (FMR) underpins intelligent flight training, including training assessment, pilot skill profiling, and flight safety monitoring. Existing FMR methods typically require large labeled datasets, generalize poorly across aircraft, and provide limited decision transparency. We propose TableManeuver, an explainable LLM-based FMR method that reformulates multivariate flight parameter time series as table-understanding inputs. The method updates no base LLM parameters and uses a small labeled training set only as a retrieval library; it is therefore not a zero-shot setting. TableManeuver first converts flight parameter sequences into tabular text that preserves temporal indices and channel semantics, reducing the mismatch between numerical time series and the textual semantic space of LLMs. It then combines domain knowledge, neighborhood sample references, and task decomposition prompts in a retrieval-augmented reasoning architecture that guides explicit step-by-step inference. We evaluate the method on a flight dataset collected from human pilots on a high-fidelity flight simulation platform. Without base LLM parameter updates, TableManeuver achieves 96.2% precision, 96.8% recall, and a 96.5% F1 score, exceeding the strongest supervised baseline by 3.5 percentage points in F1. In cross-aircraft evaluation, the F1 score decreases by only 1.4 percentage points, which is substantially smaller than the degradation observed for deep learning baselines. Retrieval-only baselines that transfer neighbor labels without LLM inference perform markedly worse, indicating that the performance gains are not explained by neighbor label transfer alone. TableManeuver combines recognition accuracy, cross-aircraft robustness, and readable step-by-step reasoning evidence, offering a practical route for applying LLMs to aviation time series analysis. Full article

The efficiency of ammunition support operations in the aircraft carrier intermediate ordnance staging deck is critical to sortie generation rates in naval aviation, yet joint scheduling and personnel allocation in this multistage, resource-constrained environment remains a challenging bi-objective optimization problem. This study develops a framework integrating an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with a marginal-benefit-based iterative feedback mechanism. The intermediate ordnance staging deck support process is decomposed into individual ammunition processing stations and formulated as a processflow model incorporating operation sequencing and personnel specialization constraints. A constraint decision model then dynamically reconciles the minimization of total makespan and personnel workload equilibrium through iterative marginal-benefit comparison across support teams. The NSGA-II is enhanced with an adaptive crossover-mutation mechanism and an improved elitism preservation strategy to strengthen global search capability. Validation on a typical carrier intermediate ordnance staging deck scenario demonstrates that the improved NSGA-II outperforms the conventional NSGA-II in convergence speed and Pareto front quality. Under the optimized configuration, the total makespan remains 3600 s with a workload balance metric of 1075 even as ammunition quantity doubles from two to four units. The proposed framework offers practical decision support for carrier ammunition operations and extends to other resource-constrained multi-objective scheduling domains. Full article

Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum and kinematic measurements; however, their heterogeneity in dimension, distribution, and physical meaning often makes direct fusion under-exploit discriminative complementarity and inadequately model temporal track evolution. To address this, we propose a Doppler-Kinematic Spatio-Temporal Graph Learning framework named Dual-Stream Spatio-Temporal Cross-Attention Graph Convolutional Network (DS-STCAGCN) for LSS target recognition using multi-dimensional radar observations. The method separately encodes Doppler-spectrum and kinematic features to preserve their modality-specific characteristics, fuses them through bidirectional cross-attention, captures long-range temporal dependencies via self-attention, and aggregates local frame-to-frame correlations through graph convolution on a time-ordered observation graph. On the public L-band digital-array dataset LSS-DAUR-1.0, DS-STCAGCN achieves 99.73% mean accuracy and maintains 98.64% at 5 dB signal-to-noise ratio (SNR). On the passive-radar dataset LSS-PR-1.0, it reaches 99.86% mean accuracy, demonstrating strong cross-modal generalization. This work provides an effective spatio-temporal modelling framework for multi-dimensional radar sensing and robust LSS target recognition. Full article

For autonomous maritime perception and situational awareness, the end-to-end multi-object tracking paradigm has achieved complete learning, from image sequences to tracking results, reducing the reliance on manually designed association rules and holding great potential. However, in maritime radar video multi-object tracking, due to the limited visual features of targets and significant feature variations under long-term tracking, problems such as identity switching are prone to occur, making it difficult to directly apply existing end-to-end approaches. To solve these problems, this paper proposes a trajectory-aware end-to-end multi-object tracking method. The real-time trajectory of the targets contains temporal context information. This work uses it as prior knowledge to enhance visual feature encoding and compensate for the shortcomings of single-frame visual features. Specifically, the trajectory feature is encoded by the trajectory encoder module while, simultaneously, the visual features are encoded through the backbone and the visual feature encoder module. Then, in the frame-trajectory cross-modal attention module, the trajectory feature encoding is used to reconstruct the visual feature encoding with cross-attention, dynamically enhancing the features related to the target identity. Experiments on actual collected maritime radar video data show that the proposed method is effective, achieving improvements in several key indicators. Full article

This study presents a novel one-dimensional solution method to demonstrate the effects of fuel composition and channel roughness on phase-change heat transfer in spiral regenerative cooling systems. The calculated models are grounded in an experimental correlation of liquefied natural gas (LNG) flow boiling, and their accuracy is validated through ignition experiments conducted on a 1 kg/s-class thrust chamber. The experimental data shows that the physical characteristics of LNG contribute to an extended reach within the two-phase region, resulting in a calculated pressure drop that exceeds that of pure liquid methane. Variations in surface roughness influence the pressure drop by altering the frictional coefficient. Specifically, an increase in surface roughness from 2 µm to 8 µm results in a 47.8% rise in pressure drop. The proposed model demonstrates high accuracy, with deviations in the coolant temperature rise and the pressure drop being less than 9.0% and 7.6%, respectively, when compared to experimental data. The findings serve as an engineering guide for designing and optimizing heat transfer in LOX/LNG rocket engine cooling systems. Full article

Synthetic aperture radar (SAR) object detection has long been affected by spatial speckle interference, spectral energy imbalance, and structural bias in cross-scale feature fusion. In this article, we propose the Multi-Domain Interference-Suppressed Detection Transformer (MDIS-DETR), a unified multi-domain interference-suppressed detection framework built on the Real-Time Detection Transformer (RT-DETR) architecture. Specifically, spatial-domain interference is suppressed by learnable fusion of complementary denoising responses at the input stage. Furthermore, frequency-domain interference is suppressed by polarization-guided attention together with adaptive frequency refinement within the encoder. In addition, structural-domain interference is suppressed by non-sequential cross-scale interaction to enhance multi-scale consistency. Extensive experiments on multiple SAR benchmarks demonstrate that MDIS-DETR establishes state-of-the-art (SOTA) performance across datasets. Notably, on SARDet-100K, currently the largest SAR detection dataset with a scale comparable to the Common Objects in Context (COCO) dataset, it achieves 58.82% mAP, surpassing the RT-DETR baseline by 4.58%. Full article

Titanium alloys have been extensively employed in the aerospace industry, and their service performance is largely governed by high-temperature low-cycle fatigue damage. However, investigations into the fatigue behavior of TC25 titanium alloy subjected to corrosion in a marine atmospheric environment remain limited. In this study, high-temperature low-cycle fatigue tests were conducted on TC25 titanium alloy before and after corrosion. It was found that, after corrosion, the proportion of the structural failure stage increased by approximately 10%. The corrosion pits on the surface led to local stress concentration, resulting in an increase in the number of fatigue crack sources and an acceleration of the fatigue crack growth rate, thus reducing the fatigue life of the material. These findings provide important theoretical and experimental support for the application of TC25 titanium alloy in marine environments. Full article

With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is difficult to adapt to the complex and changeable marine environment. Therefore, based on the RT-DETR model of transformer architecture, an improved scheme for maritime distress target detection is proposed to improve the small target recognition ability and detection efficiency. Specific improvements include: a small target-focused convolution module (SFConv) is designed to enhance the efficiency of feature extraction and reasoning of small-scale targets; The cross-scale feature interaction optimization module (SPE) is further proposed to improve the ability of multi-scale perception and background suppression; The Focaler-DIoU loss function is introduced to enhance the discrimination performance of the model for difficult samples. On the basis of maintaining the end-to-end detection advantage of RT-DETR, the improvement is of 0.83474, which is 5.7% higher than the original model (0.78964). The accuracy and robustness of the model in complex marine environment is significantly improved, and technical support is provided for the construction of an efficient and intelligent marine monitoring and emergency response system. Full article

In maritime electronic support measures (ESMS), multipath-scattered pulses are often suppressed during pulse sorting, although their delay, amplitude, and angular differences may provide information for passive scatterer localization. This paper investigates a front-end path-classification task positioned after emitter-level clustering and before multipath-assisted passive localization. Pulses produced by the same non-cooperative emitter but received through different propagation paths are classified as direct-path or multipath-scattered pulses. The task is formulated as supervised binary classification over PDW sequences. Five representative solution families are evaluated under a common protocol: FCM, DBSCAN, temporal sequence analysis (TSA), Single-LSTM, and a residual two-layer unidirectional LSTM with residual fusion. The input features are RF, PA, PW, PRI, TOA, DOA, and ΔTOA; the recurrent models use class-weighted training to address the direct/scattered class imbalance. Across 36 coupled scenarios with pulse-loss rates from 0% to 50% and parameter-jitter levels from 0.0 to 1.0, the residual LSTM obtains the highest average macro-F1 score (0.8717), compared with Single-LSTM (0.7726), DBSCAN (0.7686), TSA (0.6511), and FCM (0.5917). Repeated training over four random seeds yields a validation macro-F1 of 0.9821 ± 0.0007 on the original validation set. The ablation results indicate that ΔTOA is the principal temporal cue in this setting, while LayerNorm, residual fusion, class weighting, and augmentation mainly contribute to optimization stability and perturbation robustness. Measured-data verification suggests that the learned temporal representation can provide usable inputs for subsequent scatterer localization. The current validation is limited to a one-emitter simulation and rule-assisted measured-data annotation; mixed-emitter validation and quantitatively calibrated localization evaluation remain subjects for future study. Full article

To address the challenges of generating autonomous, high-quality control laws for high-performance Unmanned Aerial Vehicles (UAVs) performing long-horizon complex aerobatic maneuvers, specifically the difficulty of achieving energy-altitude closure, low-level control chattering, and low utilization of high-quality experience, this paper proposes an improved Soft Actor-Critic (SAC) algorithm incorporating a Quality-Aware Expert Pool (QAEP). Using the aerobatic loop maneuver as a representative scenario, this study explores the autonomous generation of control-surface manipulation policies comparable to those of skilled human pilots for agile fixed-wing UAVs. First, a singularity-free feature state representation and a rate-integrated action space are constructed. Combined with a symmetric shaping reward, these suppress control chattering at the physical level and achieve energy-altitude closure throughout the maneuver. Second, a dual-threshold expert pool driven by task reward and trajectory quality, together with a progressive mixed-sampling mechanism, is designed to effectively filter out low-quality samples and improve algorithmic convergence stability. Simulation experiments based on JSBSim with a high-fidelity F-16 model, which serves as a representative surrogate for a high-performance UAV, demonstrate that the proposed method generates maneuver strategies with manipulation quality comparable to that of skilled human pilots. The Dynamic Time Warping (DTW) similarity between the generated control commands and human expert demonstration data exceeds 0.97, the Manipulation Smoothness Index (MSI) is improved by 7.3%, and the loop completion rate under randomized initial conditions reaches 96.2%. These results suggest that the proposed framework enables human-like energy coordination and fine-grained control sequence generation in complex simulation environments, offering a promising approach to advancing maneuver intelligence and autonomous control capability in UAV systems. Full article