Search Results (38)

Autonomous Underwater Vehicles (AUVs) are widely employed for deep sea exploration and underwater operations, but their navigation performance is often degraded in complex environments due to time-varying measurement noise, abnormal observations, and Doppler Velocity Log (DVL) outages. To address these challenges, this paper proposes an integrated SINS/DVL/PS navigation framework that combines an Adaptive Huber and Sage–Husa Extended Kalman Filter (AHR-EKF) with a Nonlinear AutoRegressive with eXogenous inputs (NARX)-based velocity prediction model. The AHR-EKF effectively suppresses outliers and adapts to time-varying noise, thereby enhancing filter stability and state estimation accuracy. During DVL outages, the NARX model predicts short-term AUV velocity using propeller speed, velocity increments from the navigation system, and attitude information as exogenous inputs. This data-driven approach compensates for lag and mismatch in propeller-based velocity measurements, while capturing both short-term fluctuations and overall velocity trends. Simulations and sea trials were conducted to validate the method. In the simulation experiment during DVL outages, the V-NARX method achieved east and north positioning of RMS errors of 8.397 m and 6.530 m, compared with 24.699 m and 10.218 m for the V-RPM method. In the sea trial, the V-NARX method achieved east and north RMS errors of 41.160 m and 28.023 m, respectively, compared with 52.820 m and 67.057 m for V-RPM, corresponding to reductions of 22.1% and 58.2%. The proposed method maintains trajectory continuity and effectively suppresses rapid INS error accumulation during DVL outages, significantly enhancing emergency navigation capability under DVL outages. Although its positioning accuracy does not match that of normal DVL operation, the method provides a practical and reliable engineering solution for continuous AUV navigation when DVL is unavailable. Full article

GNSS/INS integrated navigation systems play a critical role in marine navigation, providing high-precision position and attitude information for moving platforms. However, in complex marine environments—such as occlusions caused by offshore engineering platforms—GNSS signal attenuation frequently leads to a rapid degradation of positioning accuracy. To address this issue in post-processing applications, this paper proposes an Adaptive Rauch-Tung-Striebel Smoother (ARTSS)-based GNSS/INS integrated navigation method. The proposed method first performs forward filtering using an Error-State Extended Kalman Filter (ESKF). Subsequently, an adaptive equivalent weight is dynamically constructed using the Huber M-estimation cost function based on the forward filtering innovations. This adaptive factor is utilized to dynamically modulate the smoothing gain in the backward pass, thereby effectively suppressing the interference of measurement outliers. To verify the effectiveness of the algorithm, comparative experiments are conducted using real-world land vehicle and shipborne kinematic datasets. Three methods are evaluated: the standard ESKF, the fixed-interval backward smoothing (RTSS), and the proposed ARTSS approach. The loosely coupled solutions from the Inertial Explorer (IE) software serve as the reference truth. Experimental results demonstrate that the proposed algorithm achieves significant improvements in positioning and attitude accuracy during GNSS signal outages. Specifically, compared with the conventional ESKF and RTSS methods, the 3D position accuracy of the shipborne experiment is improved by 31.07% and 6.97%, respectively, while that of the land vehicle experiment is increased by 48.05% and 8.67%. Therefore, the method presented in this paper effectively mitigates the accumulation of forward filtering errors and significantly enhances the accuracy, stability, and reliability of the integrated navigation system in complex environments. Full article

Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of $53.39\mathrm{m}$, compared with $878.75\mathrm{m}$, $58.84\mathrm{m}$, and $56.49\mathrm{m}$ for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions. Full article

Accurate transmission-line parameters are essential for reliable, efficient, and sustainable smart grid operation, especially under increasing renewable-energy integration and data-driven grid management. However, line aging, temperature variations, and measurement outliers may cause significant deviations between actual and nominal grid models, thereby degrading the state estimation, power-flow analysis, and operational security assessment. To address these challenges, this paper proposes a robust transmission-line parameter estimation method based on a variable-projection framework. The proposed framework decomposes the original high-dimensional, strongly coupled, and non-convex joint estimation problem into two subproblems associated with line-parameter identification and operating-state calibration. An iteratively reweighted least-squares algorithm based on the Huber M-estimator is introduced to dynamically adjust measurement weights and suppress the influence of outliers. The preconditioned conjugate-gradient method is further employed to avoid the explicit inversion of large-scale normal matrices. Simulations on the IEEE 118-bus system demonstrate that the proposed method achieves a higher parameter-estimation accuracy and stronger robustness than conventional weighted least-squares and joint state-parameter estimation methods. In the base case, the proposed method reduces the RMSRE of line reactance to 0.0794%, compared with 0.1558% for WLS and 0.1126% for JSE. Under the representative 5% gross-error case, the proposed method maintains lower RMSREs of 0.9772%, 0.0875%, and 5.8536% for $R_l$, $X_l$, and $B_{sh}$, respectively. Further sensitivity tests under contamination ratios from 1% to 20%, outlier magnitude factors from 1.5 to 5.0, and different outlier-location patterns confirm that the proposed method maintains a more stable estimation accuracy than WLS, conventional JSE, and Huber-JSE without VPM under diverse bad-data conditions. In downstream operational evaluations, it reduces the branch active-power flow RMSE from 1.6842 MW to 0.7215 MW, voltage-magnitude RMSE from 0.00482 p.u. to 0.00216 p.u., and active-power-loss error from 2.4368% to 0.9327% compared with WLS. These quantitative results indicate that the proposed approach can improve the grid model accuracy under imperfect measurements, thereby supporting reliable and sustainable smart-grid operation. Full article

This study proposes and evaluates a hybrid framework for predicting curling stone motion by combining physical models with machine learning. Motion capture data from a curling sheet were used to train modules for sliding trajectory prediction and post-impact collision prediction. These modules were connected in an integrated rollout from a single preprocessed-frame state 1 m before the tee line to predict resting position and in-play/out-of-play status. Huber regression was used for trajectory prediction and random forest regression for collision prediction, with hybrid variants learning residual corrections to physical-model outputs. The framework was evaluated using five-fold cross-validation. In trajectory prediction, ML and hybrid variants reduced velocity error relative to the default physical model, while the tuned physical model remained competitive for direction-angle estimation. In collision prediction, ML and hybrid models improved direction-angle and angular-velocity prediction over the perfectly elastic baseline. In the integrated simulation, 867 trials were evaluated after excluding 21 trials with both measured stones out of play. The hybrid rollout achieved the lowest stop-position MAE and SD for the colliding stone and, for the collided stone, an MAE comparable to that of the ML model with the lowest SD. These results show that residual correction of simple physics-based baselines improves local prediction and final-position stability. Full article

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, we propose a resilient cyber–physical framework that jointly optimizes robust dynamic state estimation and collaborative voltage control. At the estimation layer, a novel Persistence-Based Robust Extended Kalman Filter (PB-REKF) is developed, which employs a temporal persistence counter to adaptively switch between Huber M-estimation for sporadic outlier suppression and covariance inflation for rapid tracking of persistent state mutations. At the control layer, a chance-constrained Second-Order Cone Programming (SOCP) strategy directly embeds the real-time posterior covariance from the PB-REKF into the voltage safety constraints, creating a data-quality-adaptive security buffer that provides a $95\%$ probabilistic voltage guarantee. Simulations on 5-bus and IEEE 33-bus systems demonstrate that the proposed framework achieves a $29.5\%$ reduction in global RMSE and a $72.8\%$ reduction in peak outlier-window estimation error relative to the standard EKF, while reducing the voltage violation rate from $8.8\%$ to $3.8\%$. The complete estimation and control pipeline requires $1.341$ ms per update step, confirming real-time feasibility. Full article

To address the critical issues of geometric ill-conditioning and non-line-of-sight (NLOS) interference faced by broadcast radio positioning systems in long-distance transmission (≥200 km) and low-altitude flight scenarios (1000 m to 3000 m), this paper proposes a Differential and Robust Positioning method for Airborne Platforms (DPAP). Integrating radio differential positioning, the proposed method enhances the single-point positioning algorithm through a grid search and iteratively reweighted least squares to mitigate geometric ill-conditioning and numerical instability in low-altitude environments. Furthermore, a passive differential positioning approach is introduced to eliminate common errors using neighboring reference stations. Finally, a scenario-aware safe fusion strategy ensures that the fused solution is never inferior to the optimal sub-solution under any circumstances. Simulation results demonstrate that, under conditions involving six ground stations, user-to-station distances of no less than 200 km, and 15% of links experiencing NLOS propagation, the differential and robust positioning method achieves a positioning accuracy of 0.588 m RMS. This represents an improvement of approximately one order of magnitude compared to RSPP (12.304 m), and outperforms traditional Huber M-estimation (0.678 m) and elevation-weighted least squares methods (1.462 m). All results are based on Monte Carlo simulations; real-world validation with SDR hardware and flight tests is left for future work. This work provides a scalable, infrastructure-light backup for safe UAV operations in GNSS-hostile environments, directly supporting the emerging low-altitude economy. Full article

Under the combined pressures of climate change and irrigated cropland expansion, groundwater tables are declining rapidly across arid regions, thereby intensifying water limitation in riparian ecosystems. However, the mechanisms by which dominant riparian tree species coordinate multiple functional traits to maintain carbon–water balance remains poorly understood. This study investigated coordinated ecophysiological trait shifts of Populus euphratica Oliv. along a groundwater-depth gradient (2.19, 4.88, and 7.45 m) in the middle reaches of the Tarim River (China), hereafter referred to as shallow, middle, and deep groundwater depths, respectively. We quantified photosynthetic, hydraulic, stomatal, leaf anatomical and nutrient traits, and estimated long-term intrinsic water-use efficiency (WUE_i) from foliar δ¹³C. As the groundwater table declined, (1) photosynthetic capacity and photochemical performance decreased, whereas WUE_i increased markedly from 38.5 ± 2.9 to 54.2 ± 1.0 μmol mmol⁻¹, accompanied by the lowest transpiration rate at the deep groundwater depth (4.6 ± 0.5 mmol m⁻² s⁻¹); (2) stomatal and anatomical adjustments consistent with water-loss reduction were observed, including a significant decline in stomatal density from 93.5 ± 14.5 to 79.3 ± 17.4 pores mm⁻², and reduced stomatal size and stomatal area fraction (−20.3% and −32.7%, respectively); (3) the percentage loss of hydraulic conductivity increased, whereas sapwood-specific hydraulic conductivity declined, accompanied by greater sapwood investment relative to leaf area, with Huber value rising from 0.06 ± 0.02 to 0.11 ± 0.04 mm² cm⁻² at deep water depth; and (4) chlorophyll concentrations and leaf water content declined, whereas structural investment increased, as reflected by higher specific leaf mass and leaf dry matter content, and leaf nutrients were enriched, with total nitrogen and total phosphorus increasing by 67.1% and 42.0%, respectively. Trait-WUE_i relationships further indicated that WUE_i covaried most strongly with leaf anatomical and nutrient traits. These results demonstrate that increasing groundwater depth was associated with coordinated shifts in carbon assimilation, water-use regulation, hydraulic function, and nutrient allocation in P. euphratica. Such trait coordination may help explain how this species persists under chronic water limitation in arid riparian forests. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization (AMSHL) algorithm, a novel approach that strategically combines fingerprinting-based and geometric time-difference-of-arrival (TDoA) methods through condition-aware adaptive fusion. The proposed framework employs a 4-RIS cooperative architecture with strategically positioned panels on room walls, enabling comprehensive spatial coverage and favorable geometric diversity. AMSHL incorporates five key innovations: (1) a hybrid fingerprint database combining received signal strength indicator (RSSI) and TDoA features for enhanced location distinctiveness; (2) a multi-stage cascaded refinement process progressing from coarse fingerprinting initialization through to iterative geometric optimization; (3) an adaptive fusion mechanism that dynamically adjusts algorithm weights based on real-time channel quality assessment including signal-to-noise ratio (SNR) and geometric dilution of precision (GDOP); (4) a robust iteratively reweighted least squares (IRLS) solver with Huber M-estimation for outlier mitigation; and (5) Bayesian regularization incorporating fingerprinting estimates as informative priors. Comprehensive Monte Carlo simulations at 3.5 GHz carrier frequency with 400 MHz bandwidth demonstrate that AMSHL achieves a median localization error of 0.661 m, root-mean-squared error (RMSE) of 1.54 m, and mean-squared error (MSE) of 2.38 m², with 87.5% probability of sub-2m accuracy, representing a 4.9× improvement over conventional hybrid fingerprinting in median error and a 7.1× reduction in MSE (from 16.83 m² to 2.38 m²). An optional sigmoid-based fusion variant (AMSHL-S) further improves sub-2m accuracy to 89.4% by eliminating discrete switching artifacts. Furthermore, we provide theoretical analysis including Cramér–Rao lower bound (CRLB) derivation with an empirical MSE comparison to quantify the gap between practical algorithm performance and theoretical bounds (MSE-to-CRLB ratio of approximately $4.0\times {10}^4$), as well as a computational complexity assessment. All reported metrics have been cross-validated for internal consistency across formulas, tables, and textual descriptions; improvement factors and error statistics are verified against primary simulation outputs to ensure reproducibility. The complete simulation framework is made publicly available to facilitate reproducible research in RIS-aided positioning systems. Full article

This study addresses the persistence of avoidable failures and efficiency losses in HVAC plants by introducing a field-validated predictive maintenance (PdM) framework that estimates component-level RUL from multiyear BMS telemetry and translates forecasts into schedule-aware maintenance actions. The objective was to determine whether an LSTM ensemble with mode-aware segmentation and isotonic calibration could yield decision-quality RUL forecasts that reduce unplanned outages, downtime, and electricity use in a large Riyadh office building. Two years of 1 min BMS data from chillers, primary pumps, and AHU fans were cleaned, standardized, and segmented by operating mode; RUL labels were derived from time-stamped work orders and failure confirmations; the LSTM produced per-minute RUL estimates trained with a Huber loss, calibrated to lower quantiles, and converted to sustained triggers compared against a fixed-interval program. On the held-out test set, the model achieved a weighted MAE of 19.8 ± 2.1 h and RMSE of 29.1 ± 3.3 h, with quantile calibration error (QCE) $\le 0.06$ and lead-time accuracy (LTA; fraction of triggers whose calibrated lower-quantile RUL is $\ge$ the planning threshold) of 0.79 at a 10-day threshold. When deployed in counterfactual evaluation, triggers reduced unplanned outages by 47.6% (paired bootstrap p = 0.008) and total downtime by 41.3% (p = 0.012), and yielded a 10.6% reduction in HVAC electricity (95% CI: 7.7–13.2%) and a 9.7% decrease in total operating cost. The findings indicate that calibrated sequence models coupled to simple sustained triggers can convert routine BMS data into reliable maintenance schedules with quantifiable reliability and energy benefits. Practically, conservative calibration (q approximately 0.25) with thresholds of 10–12 days provided stable lead windows; future work should assess transferability across climates and facility types using transfer learning and integrate uncertainty-aware triggering with MPC for joint operational and maintenance optimization. Full article

The increasing demand for on-orbit servicing (OOS), such as satellite life extension and space debris removal, has highlighted the need for research into precise relative navigation between space objects. Model-based tracking (MBT) was applied using the imaging data for relative navigation, incorporating SPNv2 (Spacecraft Pose Network v2) for an initial pose estimation. Furthermore, the performance of General Loss was evaluated by applying it during the model tracking processes and comparing it with seven other robust M-estimators, including Tukey, Welsch, and Huber. The simulations were conducted in a ROS–Gazebo environment that emulated a rendezvous with the International Space Station (ISS). Six approach profiles were generated by pairing three mutually different conic-section apertures with two attitude modes—boresight locked on the ISS versus boresight fixed on the inertial origin—producing six distinct spiral trajectories that bring the chaser from 500 m to 100 m along the depth axis of the camera. General Loss achieved superior estimation accuracy in most profiles. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. In the few instances where it did not yield the very best results, the initial error arose from matching virtual edges—generated according to the sample weight distribution—to the actual edges in the image frame; notably, by the end of the simulation, when the camera reached a depth of approximately 100 m, these errors were substantially reduced. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. Full article

The Bearing–Angle algorithm effectively improves the observability of vision-based motion estimation for moving targets by combining the dimensional information of target detection frames. However, the robustness of this algorithm will be significantly reduced when the observation error increases due to sudden changes in the target motion state. To address this shortcoming, this paper proposes a visual target motion estimation algorithm called the Dynamic Bearing–Angle, which aims to improve the accuracy and robustness of target motion analysis in dynamic scenarios such as unmanned aerial vehicle (UAV). The algorithm innovatively introduces a dual robustness mechanism of dynamic noise intensity adaptation and outlier suppression based on M-estimation. By adjusting the noise covariance matrix in real time and assigning low weights to the outlier observations using the Huber weight function, the Dynamic Bearing–Angle algorithm is able to effectively cope with non-Gaussian noise and sudden target maneuvers. We validate the performance of the proposed algorithm with numerical simulations and real sensor data, and the results show that the Dynamic Bearing–Angle maintains good robustness and accuracy under different noise intensities. Full article

Background: Mistrust in professional health information may undermine population health by reducing engagement in preventive care and contributing to poorer health outcomes. Although sleep quality is a sensitive indicator of both psychosocial stress and health behavior, little is known about how mistrust influences sleep at the population level, and whether preventive health behavior mediates this relationship. Methods: A weighted cross-sectional analysis of a representative adult sample (n = 2090) from South Tyrol, Italy was conducted. Survey data included mistrust toward professional health information (Mistrust Index), five preventive health behaviors (Health Behavior Checklist, HBC), and sleep quality (Brief Pittsburgh Sleep Quality Index, B-PSQI). Associations between mistrust, behavior, and sleep were examined using multivariable linear regression, robust regression (Huber’s M-estimator), and nonparametric correlation. Results: Sociodemographic characteristics were not significantly associated with mistrust when weighted data were applied. Higher mistrust was associated with poorer sleep quality (β = 0.09, p = 0.003). Preventive health behaviors varied significantly across mistrust levels, with high-mistrust individuals less likely to report regular engagement (all p < 0.01). Regression analyses confirmed that mistrust was independently associated with poorer sleep quality, while preventive behaviors showed no significant relationship with sleep. Conclusions: Mistrust in professional health information is independently associated with poorer sleep quality and lower engagement in preventive behaviors. However, preventive behavior does not appear to mediate this relationship. These findings highlight mistrust as a direct and potentially modifiable risk factor for sleep disturbance at the population level. Full article

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability. Full article