Search Results (157)

Solid-state LiDAR systems are widely recognized for their high reliability, low cost, and lightweight design, but they encounter significant challenges in SLAM tasks due to their limited field of view and uneven horizontal scanning patterns, especially in indoor environments with geometric constraints. To address these challenges, this paper proposes SS-LIO, a precise, robust, and real-time LiDAR–Inertial odometry solution designed for solid-state LiDAR systems. SS-LIO uses uncertainty propagation in LiDAR point-cloud modeling and a tightly coupled iterative extended Kalman filter to fuse LiDAR feature points with IMU data for reliable localization. It also employs voxels to encapsulate planar features for accurate map construction. Experimental results from open-source datasets and self-collected data demonstrate that SS-LIO achieves superior accuracy and robustness compared to state-of-the-art methods, with an end-to-end drift of only 0.2 m in indoor degraded scenarios. The detailed and accurate point-cloud maps generated by SS-LIO reflect the smoothness and precision of trajectory estimation, with significantly reduced drift and deviation. These outcomes highlight the effectiveness of SS-LIO in addressing the SLAM challenges posed by solid-state LiDAR systems and its capability to produce reliable maps in complex indoor settings. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

This paper presents a novel motion planning framework for mobile robots operating in dynamic and uncertain environments, with an emphasis on accurate trajectory prediction and safe, efficient obstacle avoidance. The proposed approach integrates search-based planning with deep learning techniques to improve both robustness and interpretability. A multi-sensor perception module is designed to classify obstacles as either static or dynamic, thereby enhancing environmental awareness and planning reliability. To address the challenge of motion prediction, we introduce the K-GRU Kalman method, which first applies K-means clustering to distinguish between high-speed and low-speed dynamic obstacles, then models their trajectories using a combination of Kalman filtering and gated recurrent units (GRUs). Compared to state-of-the-art RNN and LSTM-based predictors, the proposed method achieves superior accuracy and generalization. Extensive experiments in both simulated and real-world scenarios of varying complexity demonstrate the effectiveness of the framework. The results show an average planning success rate exceeding 60%, along with notable improvements in path safety and smoothness, validating the contribution of each module within the system. Full article

This study proposes an adaptive car-following model based on the unscented Kalman filter algorithm to enable coordinated speed control in vehicle platoons and to address key limitations present in conventional car-following models. Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. The proposed adaptive model utilizes state estimation based on the unscented Kalman filter to dynamically identify each vehicle’s maximum achievable speed and to adjust inter-vehicle constraints, thereby enforcing a unified speed reference across the platoon. By estimating these maximum speeds and transmitting them to individual follower vehicles via vehicle-to-vehicle communication, the model promotes smooth acceleration and deceleration behavior, reduces headway variability, and mitigates shockwave propagation within the platoon. Simulation studies—covering both single-leader acceleration and intermittent acceleration scenarios—demonstrate that, compared with conventional car-following models, the adaptive model based on the unscented Kalman filter achieves superior speed synchronization, improved headway stability, and smoother acceleration transitions. These enhancements lead to substantial improvements in traffic flow efficiency and string stability. The proposed approach offers a practical solution for coordinated platoon speed control in intelligent transportation systems, with promising application prospects for real-world implementation. Full article

Leveraging Data Processing Units (DPUs) deployed at network interfaces, the DPU-accelerated Intrusion Detection System (IDS) enables microsecond-latency initial traffic inspection through hardware offloading. However, while generating high-throughput alerts, this mechanism amplifies the inherent redundancy and noise issues of traditional IDS systems. This paper proposes an alert correlation method using multi-similarity factor aggregation and a suffix tree model. First, alerts are preprocessed using LFDIA, employing multiple similarity factors and dynamic thresholding to cluster correlated alerts and reduce redundancy. Next, an attack intensity time series is generated and smoothed with a Kalman filter to eliminate noise and reveal attack trends. Finally, the suffix tree models attack activities, capturing key behavioral paths of high-severity alerts and identifying attacker patterns. Experimental evaluations on the CPTC-2017 and CPTC-2018 datasets validate the proposed method’s effectiveness in reducing alert redundancy, extracting critical attack behaviors, and constructing attack activity sequences. The results demonstrate that the method not only significantly reduces the number of alerts but also accurately reveals core attack characteristics, enhancing the effectiveness of network security defense strategies. Full article

Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS. Full article

This study introduces an Enhanced Error-Adaptive Extended-State Kalman Filter Model Predictive Control (EEA-ESKF-MPC) method to tackle strong coupling and inertia in supercritical power plants. By enhancing the ESKF-MPC framework with a mechanism that dynamically adjusts error weights based on real-time deviations and employs exponential smoothing, alongside a BP neural network for thermal unit simulation, the approach achieves superior performance. Simulations show reductions in the Integrated Absolute Error (IAE) for load and temperature by 3.05% and 2.46%, respectively, with a modest 0.43% pressure IAE increase compared to ESKF-MPC. Command disturbance tests and real condition tracking experiments, utilizing data from a 350 MW supercritical unit, reinforce the method’s effectiveness, highlighting its exceptional dynamic performance and precise tracking of operational parameter changes under multivariable coupling conditions, offering a scalable solution for modern power systems. Full article

Ground-based synthetic aperture radar (GBSAR) has been widely used in the fields of early warning of geologic hazards and deformation monitoring of engineering structures due to its characteristics of high spatial resolution, zero spatial baseline, and short revisit period. However, in the continuous monitoring process of GBSAR, due to the sudden failure of radar equipment, such as power failure, or the influence of alternating work between multiple regions, it often leads to discontinuous data collection, and this problem caused by missing data is collectively called “inspection mode”. The problem of missing data in the inspection mode not only destroys the spatial and temporal continuity of the data but also affects the accuracy of the subsequent deformation analysis. In order to solve this problem, in this paper, we propose a data reconstruction method that combines Sage–Husa Kalman adaptive filtering and the Rauch–Tung–Striebel (RTS) smoothing algorithm. The method is based on the principle of Kalman filtering and solves the problem of “model mismatch” caused by the fixed noise statistics of traditional Kalman filtering by dynamically adjusting the noise covariance to adapt to the non-stationary characteristics of the observed data. Subsequently, the Rauch–Tung–Striebel (RTS) smoothing algorithm is used to process the preliminary filtering results to eliminate the cumulative error during the period of missing data and recover the complete and smooth deformation time series. The experimental and simulation results show that this method successfully restores the spatial and temporal continuity of the inspection data, thus improving the overall accuracy and stability of deformation monitoring. Full article

Grid stability in microgrids represents a critical challenge, particularly with the increasing integration of variable renewable energy sources and the loss of systematic inertia. This study analyzes the use of vehicle-to-grid (V2G) technology and supercapacitors as complementary solutions to improve grid stability. A hybrid approach is proposed in which electric vehicles act as temporary storage units, supplying energy to regulate grid frequency. Supercapacitors, due to their rapid charging and discharging capabilities, are used to mitigate power fluctuations and provide immediate support during peak demand. The proposed management model integrates two strategies for frequency control, leveraging the linear relationship between power and frequency. Power smoothing is combined with Kalman filter-based frequency control, allowing for accurate estimation of the dynamic system state, even in the presence of noise or load fluctuations. This methodology improves grid stability and frequency regulation accuracy. A frequency variability analysis is also included, highlighting grid disturbance events related to renewable-energy penetration and demand changes. Furthermore, the effectiveness of the Kalman filter in improving grid stability control, ensuring an efficient dynamic response, is highlighted. The results obtained demonstrate that the combination of V2G and supercapacitors contributes significantly to reducing grid disturbances, optimizing energy efficiency, and enhancing system reliability. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. Full article

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality. Full article

Despite the pivotal role of filtering and smoothing techniques in the preprocessing of ship maneuvering data for robust identification, persistent challenges in reconciling noise suppression with dynamic fidelity preservation have limited algorithmic advancements in recent decades. We propose an online smoothing method enhanced by the Expectation-Maximization (EM) algorithm framework that effectively extracts high-fidelity dynamic features from raw maneuvering data, thereby enhancing the fidelity of subsequent ship identification systems. Our method effectively addresses the challenges posed by heavy-tailed Student-t distributed noise and parameter uncertainty inherent in ship motion data, demonstrating robust parameter learning capabilities, even when initial ship motion system parameters deviate from real conditions. Through iterative data assimilation, the algorithm adaptively calibrates noise distribution parameters while preserving motion smoothness, achieving superior accuracy in velocity and heading estimation compared to conventional Rauch–Tung–Striebel (RTS) smoothers. By integrating parameter adaptation within the smoothing framework, the proposed method reduces motion prediction errors by 23.6% in irregular sea states, as validated using real ship motion data from autonomous navigation tests. Full article

For manoeuvring surface target tracking in the presence of glint noise, Huber-based Kalman filters have been widely regarded as effective. However, when the proportion of outlier measurements is high, their numerical stability and estimation accuracy can deteriorate significantly. To address this issue, we propose a Robust Cubature Kalman Filter with the Current Statistical (RCKF_CS) model. Inspired by the Huber equivalent weight function, an adaptive factor incorporating a penalty strategy based on a smoothing approximation function is introduced to suppress the adverse effects of glint noise. The proposed method is then integrated into the Cubature Kalman Filter framework combined with the Current Statistical model. Unlike conventional Huber-based approaches, which process measurement residuals independently in each dimension, the proposed method evaluates the residuals jointly to improve robustness. Numerical stability analysis and extensive simulation experiments confirm that the proposed RCKF_CS achieves improved numerical robustness and filtering performance, even under strong glint noise conditions. Compared with existing Huber-based filters, the proposed method enhances filtering performance by 2.66% to 10.18% in manoeuvring surface target tracking tasks affected by glint noise. Full article

Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation. Full article