Search Results (31)

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

With the increasing deployment of unmanned aerial vehicles (UAVs) in indoor environments, the demand for high-precision six-degrees-of-freedom (6-DoF) localization has grown significantly. Ultra-wideband (UWB) technology has emerged as a key enabler for indoor UAV navigation due to its robustness against multipath effects and high-accuracy ranging capabilities. However, conventional UWB-based systems primarily rely on range measurements, operate at low measurement frequencies, and are incapable of providing attitude information. This paper proposes a tightly coupled error-state extended Kalman filter (TC–ESKF)-based UWB/inertial measurement unit (IMU) fusion framework. To address the challenge of initial state acquisition, a weighted nonlinear least squares (WNLS)-based initialization algorithm is proposed to rapidly estimate the UAV’s initial position and attitude under static conditions. During dynamic navigation, the system integrates time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) measurements obtained from the UWB module to refine the state estimates, thereby enhancing both positioning accuracy and attitude stability. The proposed system is evaluated through simulations and real-world indoor flight experiments. Experimental results show that the proposed algorithm outperforms representative fusion algorithms in 3D positioning and yaw estimation accuracy. Full article

In the field of tunnel engineering, the precise determination of the spatial coordinates of karst water-rich pipelines represents a critical area of research for disaster prevention and control. Traditional detection methods often exhibit limitations, including inadequate accuracy and low efficiency, which can significantly compromise the safety and quality of tunnel construction. To enhance the accuracy of the spatial coordinate estimation for karst water-rich pipelines, this study introduces a novel method grounded in a strapdown inertial navigation system (SINS). This approach involves the deployment of sensing equipment within the karst water-rich pipeline to gather motion state data. Consequently, it provides spatial coordinate information pertinent to the karst water-rich pipeline within the tunnel site, thereby augmenting the completeness and accuracy of the spatial coordinate estimation results compared to conventional detection methods. This study employs ESKF filtering to process the data collected by the SINS, ensuring the robustness and accuracy of the data. The research integrates theoretical analysis, model testing, and numerical simulation. It systematically examines the operational principles and error characteristics associated with the SINS, develops an error model for this technology, and employs a comparative selection method to design the spatial coordinate sensing equipment based on the SINS. Full article

Accurate and reliable multi-scene positioning remains a critical challenge in autonomous driving systems, as conventional fixed-noise fusion strategies struggle to handle the dynamic error characteristics of heterogeneous sensors in complex operational environments. This paper proposes a novel noise-adaptive fusion framework integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) measurements. Our key innovation lies in developing a dual noise estimation model that synergizes priori weighting with posterior variance compensation. Specifically, we establish an a priori weighting model for satellite pseudorange errors based on elevation angles and signal-to-noise ratios (SNRs), complemented by a Helmert variance component estimation for posterior refinement. For INS error modeling, we derive a bias instability noise accumulation model through Allan variance analysis. These adaptive noise estimates dynamically update both process and observation noise covariance matrices in our Error-State Kalman Filter (ESKF) implementation, enabling real-time calibration of GNSS and INS contributions. Comprehensive field experiments demonstrate two key advantages: (1) The proposed noise estimation model achieves 37.7% higher accuracy in quantifying GNSS single-point positioning uncertainties compared to conventional elevation-based weighting; (2) in unstructured environments with intermittent signal outages, the fusion system maintains an average absolute trajectory error (ATE) of less than 0.6 m, outperforming state-of-the-art fixed-weight fusion methods by 36.71% in positioning consistency. These results validate the framework’s capability to autonomously balance sensor reliability under dynamic environmental conditions, significantly enhancing positioning robustness for autonomous vehicles. 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

The series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To accurately identify and analyze the torsional vibration characteristics induced by shaft resonance in this process, a torsional vibration feature identification algorithm based on disturbance observation and parameter partition learning is proposed. A simplified model of the drivetrain shaft system is first established, and an extended state Kalman filter (ESKF) is designed to accurately estimate the torque of the torsional damper. The inclusion of extended disturbance states enhances the model’s robustness against system uncertainties. Subsequently, continuous wavelet transform (CWT) is employed to identify the resonance characteristics in the torsional vibration process from the torque signal. Combined with the parameter partition learning strategy, resonance frequencies are utilized to infer key system parameters. The results demonstrate that, under a 20% perturbation of structural parameters, the observer model with fixed parameters yields a root mean square error (RMSE) of 10.16 N·m for the torsional damper torque. In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. Using the Morlet wavelet with a frequency resolution parameter (VPO) of 15 at a 50 Hz sampling rate, the identified resonance frequency was 14.698 Hz, showing a 1.1% deviation from the actual natural frequency of 14.53 Hz. Full article

Low-cost MEMS sensors are widely utilized in UAV platforms to address attitude estimation problems due to their compact size, low power consumption, and cost-effectiveness. Diverse UAV payloads pose new challenges for attitude estimation, such as magnetic interference environments and high dynamic environments. In this paper, we propose a hierarchical decoupled attitude estimation algorithm, termed HDAEA. Initially, a novel hierarchical decoupling approach is introduced for the attitude and angle representation of the direction cosine matrix, enabling the representation of angles in a new manner. This method reduces the data dimensionality and nonlinearity of observation equations. Furthermore, a magnetic interference identification algorithm is proposed to compute the magnetic interference intensity accurately and quantitatively. Combining the quantified errors of estimated state variables, an error model for magnetic interference and attitude angles in high-dynamic environments is constructed. Subsequently, the proposed error model is employed to calibrate the hierarchical decoupled angles using accelerometer and magnetometer measurements, effectively mitigating the impact of magnetic interference on the calculation of pitch angles and roll angles. Moreover, the integration of the proposed hierarchical decoupled attitude estimation algorithm with the error-state extended Kalman filter reduces system nonlinearity and minimizes linearization errors. Experimental results demonstrate that HDAEA exhibits significantly improved attitude estimation accuracy of UAV payloads. Full article

In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled signals, and multiple interferences in measurement signals during flexible operation. To this end, this paper proposes a model predictive control (MPC) scheme based on the extended state Kalman filter (ESKF). This scheme optimizes the MPC control framework. The ESKF is used to filter the collected output signals and jointly estimate the state and disturbance quantities in real time, thus promptly establishing a prediction model that reflects the true state of the system. Subsequently, taking the minimum output signal deviation of the main steam pressure and bed temperature and the control signal increment as objectives, a coordinated receding horizon optimization is carried out to obtain the optimal control signal of the control system within each control cycle. Tracking, anti-interference, and robustness experiments were designed to compare the control effects of ESKF-MPC, ID-PI, ID-LADRC, and MPC. The research results show that, when the system parameters had a ±30% perturbation, the adjustment time range of the main steam pressure and bed temperature loops of this method were 770~1600 s and 460~1100 s, respectively, and the ITAE indicator ranges were 0.615 × 10⁵~1.74 × 10⁵ and 3.9 × 10⁶~6.75 × 10⁶, respectively. The overall indicator values were smaller and more concentrated, and the robustness was stronger. In addition, the test results of the actual continuous variable condition process of the unit show that, compared with the PI strategy, after adopting the ESKF-MPC strategy, the overshoot of the main steam pressure loop of the combustion system was small, and the output signal was stable; the fluctuation range of the bed temperature loop was small, and the signal tracking was smooth; the overall control performance of the system was significantly improved. Full article

Event-based cameras are a new type of vision sensor in which pixels operate independently and respond asynchronously to changes in brightness with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robots to deal with some challenging scenes. We propose a visual–inertial odometry for stereo event-based cameras based on Error-State Kalman Filter (ESKF). The vision module updates the pose by relying on the edge alignment of a semi-dense 3D map to a 2D image, while the IMU module updates the pose using median integration. We evaluate our method on public datasets with general 6-DoF motion (three-axis translation and three-axis rotation) and compare the results against the ground truth. We compared our results with those from other methods, demonstrating the effectiveness of our approach. Full article

IgA nephropathy (IgAN), first described in 1968, is one of the most common forms of glomerulonephritis and can progress to end-stage kidney disease (ESKD) in 25 to 30 percent of patients within 20 to 25 years from the onset. It is histologically characterized by mesangial proliferation with prominent IgA deposition. The prognosis may be difficult to predict, but important risk factors for disease progression of kidney disease have been recognized: usually proteinuria above 0.75–1 g/day with or without hematuria, hypertension, high-risk histologic features (such as crescent formation, immune deposits in the capillary loops, mesangial deposits, glomerulosclerosis, tubular atrophy, interstitial fibrosis, and vascular disease), and a reduced Glomerular Filtration Rate (GFR). In the absence of reliable specific biomarkers, current standards of care are addressed to decrease proteinuria, as a surrogate endpoint, and control blood pressure. For a long time, corticosteroids have been considered the only cure for proteinuric patients or those at risk of progression to ESKF; however, unfortunately, like other immunosuppressive agents, they are burdened with high collateral risks. Therefore, optimal treatment remains a challenge, even if, to date, clinicians have many more options available. Here, we will review the main therapies proposed, such as the stronghold of RAAS inhibition and the use of SGLT2 inhibitors; it is expected that ongoing clinical trials may find other therapies, apart from corticosteroids, that may help improve treatment, including both immunosuppressive monoclonal antibodies and other strategies. At the current time, there are no disease-specific therapies available for IgAN, because no largescale RCTs have demonstrated a reduction in mortality or in major adverse kidney or cardiovascular events with any therapy. Full article

Introduction: Acute pulmonary edema (APE) due to fluid overload is considered the most feared complication in hemodialysis patients. Various diagnostic tests have been proposed to assess the fluid status in patients with end-stage kidney failure (ESKF); among these, lung ultrasound (measuring the number of B-lines) is emerging as a promising tool to identify pulmonary congestion in this patient population. Methods: We compared the incidence of APE before and after the implementation of lung ultrasound as a routine practice in our unit. The pre (from 1 January 2007 to 31 December 2008)- and post (from 1 January 2017 to 31 December 2018)-B-line implementation periods included 98 and 108 hemodialysis patients, respectively. By accurately reviewing their electronic medical records, all episodes of APE were collected. The 10-year interval between the two periods was specifically chosen to ensure no overlap between patients of the two cohorts whereas the single-center design was adopted to minimize the influence of center effect on the study results. Results: APE episodes occurred more frequently in patients from the pre-B-line implementation group (18/98, i.e., 18.4%) compared with those from the post B-line implementation group (6/108, i.e., 5.5%) (p = 0.004). An analysis based on repeated APE events showed that the incidence rate of APE was significantly higher during the pre-implementation period (2.0 APE episodes per 100 person-months, 95% CI: 1.4–2.7) than during the post-implementation period (0.3 APE episodes per 100 person-months, 95% CI: 0.1–0.7), with an incidence rate ratio (post- versus pre-) of 0.17 (95% CI: 0.07–0.40; p < 0.001). The odds of experiencing APE episodes were 74% lower (odds ratio: 0.26, 95% CI: 0.10–0.69) in patients from the post B-line implementation period compared with those from the pre-implementation period. Notably, adjusting for potential confounders did not affect the strength of this association, which remained statistically significant (p ≤ 0.030). Finally, dominance analysis indicated that the implementation of B-lines was the primary factor explaining the difference in APE episodes between the two periods, followed by dialysis duration and intra-dialysis weight gain. Conclusions: The systematic use of lung ultrasound (a simple, easy-to-learn, rapid and non-invasive method, easily performed at the patient’s bed) in everyday clinical practice was associated with a drastic reduction in episodes of APE in hemodialysis patients. Further observational and interventional studies are needed to confirm these results. Full article

Magnetic pose tracking is a non-contact, accurate, and occlusion-free method that has been increasingly employed to track intra-corporeal medical devices such as endoscopes in computer-assisted medical interventions. In magnetic pose-tracking systems, a nonlinear estimation algorithm is needed to recover the pose information from magnetic measurements. In existing pose estimation algorithms such as the extended Kalman filter (EKF), the 3-DoF orientation in the $S^3$ manifold is normally parametrized as unit quaternions and simply treated as a vector in the Euclidean space, which causes a violation of the unity constraint of quaternions and reduces pose tracking accuracy. In this paper, a pose estimation algorithm based on the error-state Kalman filter (ESKF) is proposed to improve the accuracy and robustness of electromagnetic tracking systems. The proposed system consists of three electromagnetic coils for magnetic field generation and a tri-axial magnetic sensor attached to the target object for field measurement. A strategy of sequential coil excitation is developed to separate the magnetic fields from different coils and reject magnetic disturbances. Simulation and experiments are conducted to evaluate the pose tracking performance of the proposed ESKF algorithm, which is also compared with standard EKF and constrained EKF. It is shown that the ESKF can effectively maintain the quaternion unity and thus achieve a better tracking accuracy, i.e., a Euclidean position error of 2.23 mm and an average orientation angle error of 0.45°. The disturbance rejection performance of the electromagnetic tracking system is also experimentally validated. Full article

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications. Full article

A modified Extended State Kalman Filter (ESKF)-based Model Predictive Control (MPC) algorithm is introduced to tailor the enhanced disturbance suppression in electro-optical tracking systems. Traditional control techniques, although robust, often struggle in scenarios with concurrent internal, external disturbances, and sensor noise. The proposed algorithm effectively overcomes these limitations by precisely estimating system states and actively mitigating disturbances, thus significantly boosting noise and perturbation control resilience. The primary contributions of this study include the integration of ESKF for accurate system state and disturbance estimation in noisy environments, the embedding of an ESKF estimation-compensation loop to simulate an improved disturbance-free system, and a simplified modeling approach for the controlled device. This designed structure minimizes the reliance on extensive system identification, easing the predictive control model-based constraints. Moreover, the approach incorporates total disturbance estimation into the optimization problem, safeguarding against actuator damage and ensuring high tracking accuracy. Through rigorous simulations and experiments, the ESKF-based MPC has demonstrated enhanced model error tolerance and superior disturbance suppression capabilities. Comparative analyses under varying model parameters and external disturbances highlight its exceptional trajectory tracking performance, even in the presence of model uncertainties and external noise. Full article