Search Results (1,249)

Accurate estimation of accelerometer biases in Inertial Measurement Units (IMUs) is crucial for reliable Unmanned Aerial Vehicle (UAV) navigation, particularly in GPS-denied environments. Uncompensated biases lead to an unbounded accumulation of position error and increased velocity error, resulting in significant navigation inaccuracies. This paper examines the effects of accelerometer bias on UAV navigation accuracy and introduces a vision-aided navigation system. The proposed system integrates data from an IMU, altimeter, and optical flow sensor (OFS), employing an Extended Kalman Filter (EKF) to estimate both the accelerometer biases and the UAV position and velocity. This approach reduces the accumulation of velocity and positional errors. The efficiency of this approach was validated through simulation experiments involving a UAV navigating in circular and straight-line trajectories. Simulation results show that the proposed approach significantly enhances UAV navigation performance, providing more accurate estimates of both the state and accelerometer biases while reducing error growth through the use of vision aiding from an Optical Flow Sensor. Full article

Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility. Full article

With the rapid growth of the civil aviation industry, the aircraft towing and taxiing (ATT) system has gained significant attention in the literature. Due to its nonlinear and underactuated characteristics, the path tracking and control of the ATT system is a challenging issue. Therefore, in this paper, a robust adaptive cascade control structure is proposed for the path tracking of the ATT system. Firstly, the system description is established via kinematic and dynamics modeling, and the lumped system disturbance is derived for the controller design. Next, the robust adaptive cascade control structure consisting of the adaptive MPC and the CSMC or the RASMC-based dynamics controller is designed with the application of the extended Kalman filter. The system stability in the dynamics loop is verified through the Lyapunov method. The simulation results reveal that the combination of the adaptive MPC and the RASMC can provide a more accurate tracking performance and smoother trajectories under the given disturbances and uncertainties, while both ensuring passenger comfort and meeting the civil aviation regulations, indicating its potential in practical applications. Full article

This paper focuses on a solution of target self-positioning when a Global Navigation Satellite System (GNSS) is denied. It is composed of Inertial Navigation Systems (INS), Signals of Opportunities (SOPs), and a navigation prototype. One of the options for navigation via SOP (NAVSOP) is to utilize cellular signals, such as Long Time Evolution (LTE). When the prior information is insufficient, the location of the base station (BS) is obtained by collecting the demodulation of the downlink signal, and the synchronization signal is used for static time offset correction. In view of the large positioning error of the trilateral positioning method based on Received Signal Strength (RSS), a multi-station positioning optimization method is proposed by introducing the robust regression. Monte Carlo simulation experiments indicate that the method has improved the positioning failure and insufficient accuracy. Aiming at the influence of the state estimation errors on filtering results, the Second Order Mutual Difference (SOMD) method with the noise covariance R, which is independent of the existing Extended Kalman Filter (EKF) framework and combined with Redundant Measurement Noise Covariance Estimation (RMNCE), is applied to the model. The simulation results show that the average error of the robust model is 10.28 m, which is better than the EKF method. Finally, a vehicle test in constant speed has been carried out. The results show that the proposed model can realize self-positioning with limited BS location information, and the positioning accuracy can reach 11.68 m over a 283 m trajectory. Full article

In the field of multi-robot cooperative localization and task planning, traditional filtering algorithms encounter synchronization and consistency issues during multi-source data fusion. These challenges result in cumulative localization errors and inefficient information sharing, which limits the system’s collaborative capabilities and control accuracy. To overcome these limitations, a distributed cooperative navigation strategy is introduced. Initially, a Distributed Adaptive Extended Kalman Filter (DAEKF) is implemented, which adaptively adjusts the noise covariance matrix to effectively manage nonlinearities and multi-source noise conditions. Subsequently, a Distributed Model Predictive Control (DMPC) framework is introduced. This framework predicts and optimizes each robot’s kinematic model, thereby improving the system’s collaborative operations and dynamic decision-making capabilities. Finally, the efficacy of this strategy is confirmed through detailed simulations and robotic experiments. The simulation results for cooperative localization demonstrate that DAEKF outperforms Kalman Filter (KF) and Extended Kalman Filter (EKF) in terms of localization accuracy. In the straight-line path-tracking experiments, DAEKF effectively reduced both lateral and heading errors for both robots. For Robot 1, DAEKF reduced the lateral error Root Mean Squared Error (RMSE) by 68.87%, 27.80%, and 25.76%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 52.29%, 41.89%, and 36.47%. For Robot 2, DAEKF reduced the lateral error RMSE by 51.30%, 22.88%, and 11.60%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 39.55%, 37.15%, and 26.00%. In the curved path-tracking experiments, both robots demonstrated high trajectory conformity while traveling along a predefined path combining straight-line and circular arc segments, with lateral errors in the straight-line segments all below 0.05 m. The strategy proposed in this study significantly enhanced the precision and stability of multi-robot collaborative navigation, demonstrating strong practicality and scalability. 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

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article

This paper introduces a team of Automated Guided Vehicles (AGVs) equipped with open-source, perception-enhancing rotating devices. Each device has a set of ArUco markers, employed to compute the relative pose of other AGVs. These markers also serve as landmarks, delineating a path for the robots to follow. The authors combined various control methodologies to track the ArUco markers on another rotating device mounted on the AGVs. Behavior trees are implemented to facilitate task-switching or to respond to sudden disturbances, such as environmental obstacles. The Robot Operating System (ROS) is installed on the AGVs to manage high-level controls. The efficacy of the proposed solution is confirmed through a real experiment. This research contributes to the advancement of AGV technology and its potential applications in various fields for example in a warehouse with a restricted and known environment where AGVs can transport goods while avoiding other AGVs in the same environment. Full article

The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively. Full article

This work proposes a nonlinear modeling of a permanent magnet synchronous motor (PMSM) based on state space neural networks. The state space neural network is trained and the state variables (currents in a direct–quadrature frame and the rotational speed) are estimated by considering a robust Unscented Kalman Filter (UKF). Two contributions are presented in this work: the fist one is a nonlinear modeling structure for a PMSM based on a state space neural network that allows real-time parameter identification, and the second one is PMSM neural network training and state estimation based on a robust UKF. The robustness of the UKF is obtained by using a singular value decomposition of the covariance matrix. A comparison analysis is performed over a real PMSM motor by considering the proposed approach and a linear approximation of the nonlinear model where the states and parameters are computed by using an Extended Kalman Filter. The identified model is validated in closed loop by considering a nonlinear control strategy based on state feedback linearization. Full article

Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases in multi-vehicle interactions. This paper introduces VRDeepSafety, a novel and scalable VR simulation platform that overcomes these limitations by integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, and signal prioritization, to enhance AV accident prediction and mitigation. VRDeepSafety generates comprehensive datasets featuring synchronized multi-vehicle interactions, coordinated V2X scenarios, and diverse sensor data, including visual, LiDAR, radar, and V2X streams. Evaluated with our novel deep-learning model, VRFormer, which uniquely fuses VR sensor data with V2X using a probabilistic Bayesian inference, as well as a hierarchical Kalman and particle filter structure, VRDeepSafety achieved an 85% accident prediction accuracy (APA) at a 2 s horizon, a 17% increase in 3D object detection precision (mAP), and a 0.3 s reduction in response time, outperforming a single-vehicle baseline. Furthermore, V2X integration increased APA by 15%. Extending the prediction horizon to 3–4 s reduced APA to 70%, highlighting the trade-off between prediction time and accuracy. The VRDeepSafety high-fidelity simulation and integrated V2X provide a valuable and rigorous tool for developing safer and more responsive AVs. Full article

This study addresses the divergence issues in GPS navigation systems caused by inaccuracies in dynamic modeling and explores solutions using the extended Kalman filter (EKF). Since algorithms such as the Kalman filter (KF) and EKF rely on assumed process models that often deviate from real-world conditions, their performance in real-time applications can degrade. This paper introduces fictitious process noise as an effective remedy to mitigate divergence, demonstrating its benefits through covariance estimation and tuning factors to enhance observability and controllability, particularly for continuous differential GPS (DGPS) access. The study evaluates several motion scenarios, including stationary receivers, straight-line trajectories with constant and varying speeds, and turning trajectories. The inclusion of process noise allows the EKF to adapt to changes in direction and speed without explicitly modeling turning or acceleration dynamics. To ensure robustness, the simulations incorporate a variety of scenarios to assess the statistical reliability and real-world performance of the EKF, ensuring the findings are statistically robust and widely applicable. Simulated receivers were used to evaluate the position (P), position–velocity (PV), and position–velocity–acceleration (PVA) models. The results from both the Ordinary Least-Squares (OLS) and EKF simulations show improved vehicle trajectory tracking and demonstrate the EKF’s potential for broader navigation system applications. This paper’s novel contribution lies in its thorough analysis of the divergence issues in GPS navigation filter designs due to dynamic modeling inaccuracies, providing a systematic approach to addressing these challenges and offering new insights to improve estimation accuracy. Full article

The global navigation satellite system (GNSS) struggles to deliver the precision and reliability required for positioning, navigation, and timing (PNT) services in environments with severe interference. Fifth-generation (5G) cellular networks, with their low latency, high bandwidth, and large capacity, offer a robust communication infrastructure, enabling 5G base stations (BSs) to extend coverage into regions where traditional GNSSs face significant challenges. However, frequent multi-sensor faults, including missing alarm thresholds, uncontrolled error accumulation, and delayed warnings, hinder the adaptability of navigation systems to the dynamic multi-source information of complex scenarios. This study introduces an advanced, tightly coupled GNSS/5G/IMU integration framework designed for distributed PNT systems, providing all-source fault detection with weighted, robust adaptive filtering. A weighted, robust adaptive filter (MCC-WRAF), grounded in the maximum correntropy criterion, was developed to suppress fault propagation, relax Gaussian noise constraints, and improve the efficiency of observational weight distribution in multi-source fusion scenarios. Moreover, we derived the intrinsic relationships of filtering innovations within wireless measurement models and proposed a time-sequential, observation-driven full-source FDE and sensor recovery validation strategy. This approach employs a sliding window which expands innovation vectors temporally based on source encoding, enabling real-time validation of isolated faulty sensors and adaptive adjustment of observational data in integrated navigation solutions. Additionally, a covariance-optimal, inflation-based integrity protection mechanism was introduced, offering rigorous evaluations of distributed PNT service availability. The experimental validation was carried out in a typical outdoor scenario, and the results highlight the proposed method’s ability to mitigate undetected fault impacts, improve detection sensitivity, and significantly reduce alarm response times across step, ramp, and multi-fault mixed scenarios. Additionally, the dynamic positioning accuracy of the fusion navigation system improved to 0.83 m ($1\sigma$). Compared with standard Kalman filtering (EKF) and advanced multi-rate Kalman filtering (MRAKF), the proposed algorithm achieved 28.3% and 53.1% improvements in its $1\sigma$ error, respectively, significantly enhancing the accuracy and reliability of the multi-source fusion navigation system. Full article

A significant part of modern natural sciences aims to establish a model-based approach to describe the behavior of physical systems and forecast their dynamics in different scenarios. The successful application of model-based analysis of transport phenomena is driven by several components, such as the consistency of a model and the uncertainty associated with its parameters. The unsatisfactory results of simulations can be caused by an improper choice of numerical methods used, but more importantly, it can be a result of wrong assumptions while establishing the model and a poor choice of closure parameters such as physical properties of fluids. This motivated the development of a hybrid approach that combines model identification directly from the data and subsequent real-time parameter estimation, which eventually minimizes the uncertainty of the developed model. This essentially brings a new model-based approach for an optimal simulation of physical phenomena by incorporating stringent interactions between all the stages of the modeling process. The identification of the governing equation from the data is achieved by a regression technique, while the model refinement is performed using the extended Kalman filter algorithm. The obtained in such a way model is then applied for control-oriented analysis. This paper discusses the deployment of such an integrated approach on a step-to-step basis and demonstrates its application to the problem of a single-phase oil inflow to the producing well. Full article

This work presents an algorithm to perform autonomous navigation in spacecraft using onboard magnetometer data during GPS outages. An Extended Kalman Filter (EKF) exploiting magnetic field measurements is combined with a Single-Hidden-Layer Feedforward Neural Network (SLFN) trained via the Extreme Learning Machine to improve the accuracy of the state estimate. The SLFN is trained using GPS data when available and predicts the state correction to be applied to the EKF estimates. The CHAOS-7 magnetic field model is used to generate the magnetometer measurements, while a 13th-order IGRF model is exploited by the EKF. Tests on simulated data showed that the algorithm improved the state estimate provided by the EKF by a factor of 2.4 for a total of 51 days when trained on 5 days of GPS data. Full article