Search Results (105)

Zinc oxide nanowires are often used to improve the bulletproof performance of high-performance fabrics, but determining the coefficients of inter-yarn friction (CIFs) of those fabrics in numerical ballistic models is a challenge. In this article, the linear method is adopted to obtain the CIF of sateen fabrics with two thread densities treated with zinc oxide nanowires. For treated sateen fabrics with a thread density of 8 ends/cm (S-8-ZnO), the coefficient of static friction (CSF) and coefficient of kinetic friction (CKF) obtained by the linear method are 1.85 and 1.83, respectively. For treated sateen fabrics with a thread density of 13 ends/cm (S-13-ZnO), the CSF and CKF obtained by the linear method are 0.76 and 0.74, respectively. The obtained coefficients are input into the yarn pull-out models of the above two types of sateen fabrics. It is found that for both S-8-ZnO and S-13-ZnO fabrics, the errors of the yarn pull-out force by the linear method are 0.43% and 6.56%, respectively. The method presented in this study provides a more feasible approach for determining the CIF of chemically treated fabrics in future FE simulations. Full article

Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios. Full article

Nonlinear filtering algorithms have significant implications in the optimal estimation of navigation states and in improving the accuracy, reliability, and robustness of navigation systems. This manuscript surveys the developments of the nonlinear filtering algorithms (extended Kalman filtering (EKF), unscented Kalman filtering (UKF), Cubature Kalman filtering (CKF), particle filtering (PF), neural network filtering (NNF)) and adaptive/robust KF in integrated navigation systems. The principle, application, and existing problems of these nonlinear filtering algorithms are mainly studied, and the comparative analysis and prospect are carried out. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

Target tracking in typical radar applications faces critical challenges in complex environments, including nonlinear dynamics, non-Gaussian noise, and sensor outliers. Current robustness-enhanced approaches remain constrained by empirical kernel tuning and computational trade-offs, failing to achieve balanced noise suppression and real-time efficiency. To address these limitations, this paper proposes the variational Bayesian-based maximum correntropy criterion cubature Kalman filter (VBMCC-CKF), which integrates variational Bayesian inference with CKF to establish a fully adaptive robust filtering framework for nonlinear systems. The core innovation lies in constructing a joint estimation framework of state and kernel size, where the kernel size is modeled as an inverse-gamma distributed random variable. Leveraging the conjugate properties of Gaussian-inverse gamma distributions, the method synchronously optimizes the state posterior distribution and kernel size parameters via variational Bayesian inference, eliminating reliance on manual empirical adjustments inherent to conventional correntropy-based filters. Simulation confirms the robust performance of the VBMCC-CKF framework in both single and multi-target tracking under non-Gaussian noise conditions. For the single-target case, it achieves a reduction in trajectory average root mean square error (Avg-RMSE) by at least 14.33% compared to benchmark methods while maintaining real-time computational efficiency. Integrated with multi-Bernoulli filtering, the method achieves a 40% lower optimal subpattern assignment (OSPA) distance even under 10-fold covariance mutations, accompanied by superior hit rates (HRs) and minimal trajectory position RMSEs in cluttered environments. These results substantiate its precision and adaptability for dynamic tracking scenarios. Full article

Accurate dynamic estimation is of vital importance for the real-time monitoring of the operating status of power systems. To address issues such as non-Gaussian noise and outlier interference, a cubature Kalman filter state estimation method based on robust functions (RF-CKF) is proposed. Firstly, based on the exponential absolute value, an estimator is established, which is represented by the exponential absolute value and quadratic functions. Secondly, the regression form of batch processing mode is established, and the estimator based on the exponential absolute value is integrated into the cubature Kalman filter framework. Finally, an example of a standard IEEE 39-bus system is used to verify the effectiveness of the proposed method. Compared with the unscented Kalman filter, cubature Kalman filter and H-infinity CKF, the proposed method has better estimation accuracy and stronger robustness in an anomaly environment. Full article

In ground-based bearing-only tracking of multiple maneuvering targets, there are difficulties in data association due to the reliance solely on azimuth information, making it challenging to distinguish and identify multiple targets. This problem is particularly pronounced when targets are close or overlapping, leading to disassociation or target loss. Moreover, bearing-only information struggles to accurately capture the dynamic changes in maneuvering targets, significantly affecting tracking accuracy. To address these issues, this paper proposes an Improved Maneuver Detection-Based Multiple Hypothesis Bearing-Only Target Tracking (IMD-MHRPCKF) algorithm. To begin with, the observation range is segmented into multiple sub-intervals through a distance parameterization technique, and within each sub-interval, a Cubature Kalman Filter (CKF) is applied. The Multiple Hypothesis Tracking (MHT) algorithm is then used for data association, solving the measurement ambiguity problem. To detect target maneuvers, the sliding window average of the innovation sequence is calculated. When a target maneuver is detected, the sub-filter parameters are reinitialized to ensure filter stability. In contrast, if no maneuver is detected, the filter parameters remain unchanged. Finally, simulations are used to compare this algorithm with various other algorithms. The results show that the proposed algorithm significantly improves system robustness, reduces tracking errors, and effectively tracks bearing-only multiple maneuvering targets. Full article

Precise and real-time estimation of vehicle mass and road slope plays a pivotal role in attaining accurate vehicle control. Currently, road slope estimation predominantly emphasizes longitudinal slopes, with limited research on intricate slopes that include both longitudinal roads and continuous turning up-and-down slopes. To address the limitations in existing road slope estimation research, this paper puts forward a novel joint-estimation approach for vehicle mass and road slope. Vehicle mass is initially estimated via M-estimation and recursive least squares with a forgetting factor (FFRLS). A road slope estimate approach, which utilizes interacting multiple models (IMM) and cubature Kalman filtering (CKF), is proposed for complex road slope scenarios. This algorithm integrates kinematic and dynamic vehicle models within the multi-model (MM) ensemble of the IMM filter. The kinematic vehicle model is appropriate for longitudinal road gradients, whereas the dynamic vehicle model is better suited for continuous turning up-and-down slope conditions. The IMM filter employs a stochastic process to weight the appropriate vehicle model according to the driving conditions. Consequently, the weights assigned by the IMM filter enable the algorithm to adaptively select the most suitable vehicle model, leading to more accurate slope estimates under complex conditions compared to single-model-based algorithms. Simulations were carried out using Matlab/Simulink2020-Trucksim2020 to verify the effectiveness of the proposed estimation approach. The results demonstrate that, compared with existing methods, the proposed estimation approach has achieved an improvement in the precision of evaluating vehicle mass and road gradient, thus confirming its superiority. Full article

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions. Full article

Simultaneous localization and mapping (SLAM) faces significant challenges due to high computational costs, low accuracy, and instability, which are particularly problematic because SLAM systems often operate in real-time environments where timely and precise state estimation is crucial. High computational costs can lead to delays, low accuracy can result in incorrect mapping and localization, and instability can make the entire system unreliable, especially in dynamic or complex environments. As the state-space dimension increases, the filtering error of the standard cubature Kalman filter (CKF) grows, leading to difficulties in multiplicative noise propagation and instability in state estimation results. To address these issues, this paper proposes an extended-dimensional embedded CKF based on truncated singular-value decomposition (TSVD-AECKF). Firstly, singular-value decomposition (SVD) is employed instead of the Cholesky decomposition in the standard CKF to mitigate the non-positive definiteness of the state covariance matrix. Considering the effect of small singular values on the stability of state estimation, a method is provided to truncate singular values by determining the truncation threshold using the Akaike information criterion (AIC). Furthermore, the system noise is embedded into the state variables, and an embedding volume criterion is used to improve the conventional CKF while extending the dimensionality. Finally, the proposed algorithm was validated and analyzed through both simulations and real-world experiments. The results indicate that the proposed method effectively mitigates the increase in localization error as the state-space dimension grows, enhancing time efficiency by 55.54%, and improving accuracy by 35.13% compared to the standard CKF algorithm, thereby enhancing the robustness and stability of mapping. Full article

This study proposes a dual foot-mounted localisation scheme with a minimum-distance-constraint (MDC) Kalman filter (KF) for human localisation under coloured measurement noise (CMN). The dual foot-mounted localisation employs inertial measurement unit (IMUs), one on each foot, and is intended for human navigation. The KF under CMN (cKF) is then derived from the data-fusion model of the proposed navigation scheme. Finally, the MDC condition is designed and an MDC–cKF model is proposed to reduce the error in the IMUs. Empirical results showed that the proposed method effectively improves the navigation accuracy from that of MDC–KF, which neglects the effect of CMN. Full article

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands. Full article

This paper addresses the collaborative localization problem for unmanned surface vehicle (USV) clusters with random measurement delays. We propose a Cubature Kalman Hybrid Consensus Filter (CKHCF) based on the cubature Kalman filter (CKF) for widely distributed USV clusters lacking global communication capabilities. In this approach, each USV exchanges two pairs of information with all its neighbors and recalculates the received localization data based on distance and relative angle measurements. The recalculated information is then fused with the locally filtered data and updated to obtain localization information based on global measurements. To mitigate the impact of random measurement delays, we employ one-step prediction to compensate for delayed measurements. We present the derivation of the CKHCF algorithm and prove its consistency and boundedness using mathematical induction. Finally, we validate the effectiveness of the proposed algorithm through simulation experiments. Full article

Developing environmentally friendly bulk materials capable of easily and thoroughly removing trace amounts of dye pollutants from water to rapidly obtain clean water has always been a goal pursued by researchers. Herein, a green material with a 3D architecture and with strong underwater rebounding and fatigue resistance ability was prepared by means of the assembly of biopolymer chitosan (CS) and natural caraganate fibers (CKFs) under freezing conditions. The CKFs can randomly and uniformly distribute in the lamellar structure formed during the freezing process of CS and CKFs, playing a role similar to that of “steel bars” in concrete, thus providing longitudinal support for the 3D-architecture material. The 2D layers formed by CS and CKFs as the main basic units can provide the material with a higher strength. The 3D-architecture material can bear the compressive force of a weight underwater for multiple cycles, meeting the requirements for water purification. The underwater compression test shows that the 3D-architecture material can quickly rebound to its original shape after removing the stress. This 3D-architecture material can be used to purify dye-containing water. When its dosage is 3 g/L, the material can remove 99.65% of the Congo Red (CR) in a 50 mg/L dye solution. The adsorption performance of the 3D architecture adsorbent for CR removal in actual water samples (i.e., tap water, seawater) is superior than that of commercial activated carbon. Due to its porous block characteristics, this material can be used for the continuous and efficient treatment of wastewater containing trace amounts of CR dye to obtain pure clean water, meaning that it has great potential for the effective purification of dye wastewater. Full article

The trajectory estimation of a highly maneuvering target is a challenging problem and has practical applications. The interacting multiple model (IMM) filter is a well-established filtering algorithm for the trajectory estimation of maneuvering targets. In this study, we present an overview of IMM filtering algorithms for tracking a highly-maneuverable fighter aircraft using an air moving target indicator (AMTI) radar on another aircraft. This problem is a nonlinear filtering problem due to nonlinearities in the dynamic and measurement models. We first describe single-model nonlinear filtering algorithms: the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). Then, we summarize the IMM-based EKF (IMM-EKF), IMM-based UKF (IMM-UKF), and IMM-based CKF (CKF). In order to compare the state estimation accuracies of the IMM-based filters, we present a derivation of the posterior Cramér-Rao lower bound (PCRLB). We consider fighter aircraft traveling with accelerations 3g, 4g, 5g, and 6g and present numerical results for state estimation accuracy and computational cost under various operating conditions. Our results show that under normal operating conditions, the three IMM-based filters have nearly the same accuracy. This is due to the accuracy of the measurements of the AMTI radar and the high data rate. Full article