Search Results (292)

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

The concept of virtual water is currently one of the most important issues in water resource management, especially in a context marked by structural water scarcity. Beyond the analysis of virtual water flows, which has been widely studied in the literature, this study aims to better understand the relationship between virtual water exports and economic growth. This paper analyzes the dynamic relationship between Morocco’s economic growth and agricultural virtual water exports to the European Union over the period of 1986–2023. An ARDL model was used based on annual data to test cointegration and estimate short- and long-term effects, controlling for gross fixed capital formation and agricultural value added. The bounds test confirms the existence of a stable long-term relationship between the variables. The results suggest that export specialization may be associated with foreign earnings and agricultural activity while also coinciding with greater pressure on resources and potential adaptation costs, especially for blue water resources. However, estimates indicate that in the long term, investment is positively and significantly associated with growth, while virtual water exports are associated with a negative effect on GDP, suggesting that export gains may be offset by increasing water constraints and sectoral trade-offs, and that agricultural value added mainly influences short-term dynamics. The results highlight the importance of integrating water footprint and virtual water trade concepts, as well as climate constraints, into agricultural and trade strategy planning while strengthening policies on water efficiency, innovation, and governance. Full article

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions. Full article

Autonomous driving systems face critical perception failures in dense fog, where conventional RGB cameras suffer from severe degradation due to atmospheric scattering and reduced visibility. This paper presents an adaptive multi-modal fusion framework that synergistically integrates gated imaging with 3D LiDAR point clouds to achieve robust obstacle detection under visibility conditions as low as 50 m. Unlike standard cameras that passively capture scattered ambient light, gated cameras employ time-synchronized active illumination to physically filter backscattered photons, preserving structural features even in low-visibility scenarios. We propose a novel Adaptive Feature-Weighting Network (AFW-Net) that dynamically adjusts sensor modality contributions based on real-time environmental degradation assessment. The framework incorporates three key innovations: (1) a cross-modal feature extraction module that exploits the complementary physical properties of gated imaging and LiDAR, (2) an attention-based adaptive fusion mechanism that quantifies per-modality reliability through uncertainty estimation, and (3) a degradation-aware training strategy using weather-specific augmentation. Extensive experiments on the Princeton Automated Driving Dataset demonstrate that our approach maintains detection average precision (AP) above 82% under dense fog conditions (50 m visibility), representing a 23.7% improvement over state-of-the-art RGB-LiDAR fusion methods that exhibit substantial performance degradation to 58.4% AP. Ablation studies validate the necessity of each component, and cross-dataset evaluation confirms the generalization capability of the proposed framework. The adaptive weighting mechanism proves particularly effective, dynamically rebalancing modality contributions across the gated imaging and LiDAR branches while maintaining LiDAR geometric constraints. This work establishes a robust perception paradigm for safety-critical autonomous systems operating in low-visibility environmental conditions. Full article

High-performance control of surface-mounted permanent magnet synchronous motors (SPMSMs) is critical for unmanned aerial vehicle (UAV) rotor servo systems, which demand fast dynamic response, high steady-state accuracy, and strong robustness against complex disturbances. However, conventional sliding mode control (SMC) methods often suffer from inherent issues like integral windup, persistent chattering, and sensitivity to parameter variations, limiting their effectiveness in such challenging applications. To address these limitations, this paper proposes a novel composite control strategy. The method integrates an improved terminal integral sliding mode controller (ITISMC) with an adaptive super-twisting reaching law (ADSTA) and a terminal integral sliding mode observer (TISMO). The key innovations include: (1) a redesigned sliding surface incorporating a smooth nonlinear function to suppress chattering and a variable-gain integral term to mitigate integral windup; (2) an adaptive reaching law that dynamically adjusts its gains based on the system state to balance convergence speed and chattering suppression; and (3) a disturbance observer that provides real-time estimation and feedforward compensation of total disturbances, significantly enhancing robustness. The proposed ITISMC-ADSTA-TISMO strategy was implemented and validated on a TMS320F28379D DSP-based experimental platform. Comparative results demonstrate its superiority over benchmark methods (e.g., SMC-STA). Key achievements include a rapid no-load startup time of 0.45 s, high steady-state precision with speed fluctuations suppressed to only 3 rpm, and superior disturbance rejection capability under sudden load changes, sinusoidal disturbances, and parameter perturbations. The method also yields favorable q-axis current response. These results confirm that the proposed strategy offers a high-performance, practical solution for advanced UAV servo control systems. Full article

Against the backdrop of intensifying global uncertainties, enhancing the livelihood resilience of urban and rural residents is of paramount importance for promoting balanced regional development. This research establishes a 29-indicator evaluation system based on a three-dimensional analytical framework encompassing “buffering, adaptive, and transformative capacities”. resilience capacities. Utilizing county-level panel data from five pivotal former revolutionary base areas, specifically the Jiangxi–Fujian–Guangdong Former Central Soviet Area, Sichuan–Shaanxi Revolutionary Base Area, Shaanxi–Gansu–Ningxia Revolutionary Base Area, Dabie Mountains Revolutionary Base Area, and Zuojiang–Youjiang Revolutionary Base Area regions spanning from 2011 to 2023, through the integrated application of methodologies, including entropy weighting, kernel density estimation, the Theil index, and convergence analysis, we systematically examine the spatio-temporal variations and evolutionary mechanisms of livelihood resilience. Research findings indicate a general enhancement of livelihood resilience in old revolutionary base areas, albeit with notable regional disparities, presenting a tiered pattern characterized by Jiangxi–Fujian–Guangdong leading, Dabie Mountains and Sichuan–Shaanxi regions being intermediate, while Shaanxi–Gansu–Ningxia and Zuojiang–Youjiang areas lag behind. Buffering capacity predominates, while regenerative capacity constitutes the critical driver of regional disparities. The overall regional disparities are primarily driven by internal differences, with significant conditional β-convergence observed in livelihood resilience. This study proposes sustained advancement in infrastructure development to consolidate buffering capacity, a reinforcement of public services and technological innovation to enhance adaptive and regenerative capabilities, and the implementation of differentiated governance strategies, thereby fostering an overall improvement in livelihood resilience and coordinated regional development in old revolutionary base areas. Full article

In response to increasingly complex risks and challenges and to safeguard national agricultural product supply security, this study constructs a four-dimensional evaluation index system encompassing “Resistance-Adaptation-Recovery-Innovation”. Utilizing panel data from 30 provincial-level regions in China from 2017 to 2023, and employing a comprehensive methodology including the entropy method, Dagum Gini coefficient, Markov chain, kernel density estimation, and convergence models, this research measures the resilience of China’s agricultural product supply chain and investigates its spatiotemporal evolution patterns. The findings are as follows: Firstly, the resilience level of the national agricultural product supply chain shows overall steady improvement, but regional development is uneven, presenting a pattern of eastern regions leading, central regions maintaining steady progress, and western regions catching up. Secondly, the overall resilience difference is strongly correlated with regional variability, with the most pronounced internal disparity observed in the western region. Thirdly, the evolution of resilience exhibits path dependency characterized by the coexistence of a “low-level trap” and “high-level stability”, and less developed regions demonstrate a significant “catch-up effect” towards their more developed counterparts. Based on these findings, this study proposes countermeasures such as implementing targeted policies for different regions, establishing cross-regional coordination mechanisms, strengthening dynamic monitoring and early warning systems, and promoting innovation-driven development and structural upgrading. These efforts aim not only to enhance China’s capacity to respond to risks in its agricultural product supply chain and ensure national food security, but also to provide valuable insights for other countries facing similar challenges in building resilient agricultural systems in an increasingly uncertain global environment. Full article

To provide a ground-based experimental reference for free-space optical time–frequency synchronization in future space applications, this paper investigates the impact of beam drift and dynamic link-state variations on free-space laser links. A bidirectional free-space laser time–frequency synchronization and ranging system is established and the synchronization process is uniformly modeled. An improved Kalman filtering method based on innovation consistency is proposed in which a strong tracking mechanism enhances adaptability to model mismatch and abnormal observations; at the same time, an adaptive observation noise modeling strategy based on online statistical estimation characterizes the time-varying noise properties of free-space optical links. Experimental validation is conducted using an equivalent free-space laser link of approximately 321 m. The results show that the proposed method improves the time synchronization accuracy from 78.32 ps to 45.64 ps, corresponding to an enhancement of about 41%. In terms of time stability, the time deviation (TDEV) is reduced from $7.14\times {10}^{-11}$ s to $4.33\times {10}^{-11}$ s at an averaging time of $\tau =1$ s, and from $4.20\times {10}^{-12}$ s to $7.01\times {10}^{-13}$ s at $\tau =800$ s. For ranging performance, the system achieves an average measured distance of 321.56 m with a ranging standard deviation of 15.2 mm. These results demonstrate that the proposed approach enables high-precision and stable state estimation for integrated free-space laser time–frequency synchronization and ranging systems. Full article

Sensorless permanent magnet synchronous motor (PMSM) drives rely on accurate rotor electrical angle and speed estimation, vulnerable to noisy currents, quantization, and sensor biases. Fixed-bandwidth phase-locked loops (PLLs) entail an intrinsic trade-off between fast transient tracking and high-frequency noise rejection. This paper proposes an adaptive PLL based on linear active disturbance rejection control (LADRC), where a virtual coordinate formulation treats electrical-angle mismatch as a lumped disturbance estimated online by a linear extended state observer (LESO). The observer bandwidth dynamically adapts to the LESO innovation. To optimize performance, adaptive-law parameters are tuned offline via success-history adaptive differential evolution with linear population size reduction (L-SHADE). Comparative simulations against a proportional-integral PLL indicate substantially improved robustness to measurement noise, analog-to-digital quantization, and current-sensor DC offset. Specifically, the speed root-mean-square error decreases from $68.9\mathrm{r}/\min$ to $20.7\mathrm{r}/\min$ under $0.15\mathrm{A}$ additive noise, and from $1.55\mathrm{r}/\min$ to $0.48\mathrm{r}/\min$ under 12-bit quantization at $200\mathrm{r}/\min$. These enhancements reduce reliance on high-precision sensing hardware, offering a practical solution for low-cost, highly reliable motor control in complex industrial environments. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

Accurate and reliable positioning for urban vehicles remains challenging under urban canyon conditions, where Global Navigation Satellite System (GNSS) observations are frequently degraded by multipath, blockage, intermittent outages, and unstable recovery after signal reacquisition. An Inertial Navigation System (INS) can provide continuous short-term motion estimation, but its solution gradually drifts over time. Therefore, robust INS/GNSS integration is essential for urban vehicle positioning. However, in position-only fusion, contaminated GNSS positions can directly distort the integrated positioning solution. Conventional fixed-covariance filters and covariance-only adaptive filters are often insufficient to handle urban GNSS errors that are simultaneously time-varying, bias-like, and phase-dependent. To address this issue, this paper proposes a dual-adaptive robust cubature Kalman filter (Dual-ACKF) for urban vehicle INS/GNSS integration under degraded GNSS conditions. Unlike conventional adaptive CKF/UKF methods that mainly regulate the measurement-noise covariance, the proposed Dual-ACKF jointly introduces an explicit GNSS positioning bias state, a slave innovation-energy-based measurement-noise estimator, and scenario-aware robust update strategies for canyon, outage, and recovery conditions. The proposed method is validated using a challenging real-world UrbanNav sequence with Real-Time Kinematic (RTK)-derived reference trajectories and quality-defined GNSS degradation segments. Compared with Dual-AUKF, CKF, and UKF, the proposed Dual-ACKF reduces the P95 horizontal error in the outage segment from 521.23 m, 582.72 m, and 591.60 m to 228.21 m, corresponding to reductions of 56.22%, 60.84%, and 61.43%, respectively. It also reduces the maximum outage error from 638.02 m, 707.37 m, and 718.78 m to 246.45 m, demonstrating stronger long-tail error suppression during degraded and recovery-related periods. These results indicate that explicitly coupling GNSS bias absorption, online measurement-confidence regulation, and phase-dependent robust updates improves the reliability of position-only INS/GNSS integration in challenging urban environments. Full article

Against the backdrop of intensifying global climate change, reducing carbon emissions has become a shared global objective. Blue carbon, as a significant carbon sink type, still lacks a mature assessment framework. Monitoring carbon fluxes in marine salt marsh wetlands is a core technology for accurately evaluating blue carbon potential. In response, this study independently developed a spatiotemporally coupled carbon flux monitoring system for marine salt marsh wetlands. The system consists of real-time monitoring equipment, a cloud-based intelligent storage and visualization analysis platform, and a terminal assessment system. It enables the real-time monitoring of carbon fluxes across multiple spatial scales and integrates time-series patterns to assess carbon sequestration potential from multiple dimensions. To address the bottleneck of sensor accuracy, a multi-algorithm fusion technology was innovatively developed, significantly enhancing the accuracy of monitoring data. A modular integrated design was employed to construct a land–air integrated monitoring architecture, which is adaptable to the complex environments of salt marsh wetlands. This facilitates long-term automated monitoring while reducing the need for manual intervention. The terminal assessment system processes spatial-scale data using the DeNitrification-DeComposition model (DNDC 9.5) and integrates time-series carbon flux patterns, enabling precise quantification of marine carbon sink potential through spatiotemporal comprehensive analysis. The system first completed performance verification during the experimental phase, acquiring a total of 5760 sets of valid monitoring data, with a data qualification rate of 99.72%. The proposed multi-algorithm fusion method kept monitoring data fluctuations within 0.5%, and the relative error of the spatiotemporal integrated prediction was as low as 0.31%, thereby ensuring the stability and accuracy of long-term in situ monitoring. Based on this, a one-year field validation was conducted in a 100-hectare coastal salt marsh wetland in Dafeng, Yancheng. Using a spatiotemporal coupling assessment, the annual total carbon sequestration of this area was estimated at 1498.4 tons of carbon, with an assessment error of only 5.1%, achieving precise quantification of the blue carbon sink in the salt marsh wetland. This study provides reliable technical support for evaluating the carbon sequestration capacity of coastal salt marsh wetlands, contributing to the implementation of carbon emission reduction strategies. It also offers a scientific basis for global carbon cycle research and carbon sink management decision-making. Full article

In real-world scenarios, large-scale datasets often exhibit a long-tailed data distribution. Training deep neural networks on such data typically leads to a bias towards head classes. Existing studies have demonstrated that the reweighting strategy is an effective means to alleviate the long-tailed issue. Recent studies suggest that incorporating class difficulty into reweighting can yield superior results. However, the method of quantifying class difficulty by an independent validation set has shown limitations in practical applications, i.e., wasting training samples and inaccurate estimations. To address this issue, this study proposes a novel model based on K-fold cross-validation, called the adaptive combination validation model, which contains two main innovations: first, both class and sample difficulty are quantified by using a more comprehensive and authentic estimation strategy, i.e., K-fold cross-validation, to obtain accurate and robust estimations; second, we extract the prediction probability distributions of samples, which reflect sample difficulty, from different model branches and design a distribution-harmonized loss to simultaneously focus on the effects of reweighted and original distributions. Extensive experiments on several popular long-tailed image recognition datasets (CIFAR10-LT and CIFAR100-LT, with several varying imbalance rates, and ImageNet-LT) demonstrate that the proposed method can effectively alleviate the long-tailed issue and achieve state-of-the-art performance on most datasets. Full article