Search Results (38)

Rotational modulation improves strapdown inertial navigation system (SINS) by periodically reorienting the inertial measurement unit (IMU) to convert slowly varying sensor errors into manageable, cancelable components. However, existing dual-axis schemes may accumulate large total rotation angles and introduce delayed error balancing, which results in non-negligible residual attitude errors and degrades real-time navigation accuracy. To overcome these limitations, we propose an odd-symmetric dual-axis rotation strategy that jointly optimizes the rotation order and dwell positions to maximize error cancellation on each axis and across axes while constraining cumulative rotation. Based on this principle, we design a 64-position rotation scheme and derive its IMU error modulation/suppression characteristics, including gyroscope drift, accelerometer bias, scale-factor errors, and misalignment (installation) errors, and we quantify their effects on attitude and velocity. Simulations show that the proposed scheme reduces position and velocity errors by more than $60\%$ compared to a 16-position scheme, and decreases longitude error, east-velocity error, and yaw error by more than $30\%$ relative to a 32-position scheme. Experiments further validate consistent improvements in position, velocity, and attitude accuracy, demonstrating the effectiveness of the proposed rotational design for dual-axis SINS. Full article

The integration of an inertial navigation system (INS) with the Global Navigation Satellite System (GNSS) is crucial for suppressing the error drift of the INS. However, traditional fusion methods based on the extended Kalman filter (EKF) suffer from geometric inconsistency, leading to biased estimates—a problem markedly exacerbated under large initial misalignment angles. The invariant extended Kalman filter (IEKF) embeds the state in the Lie group SE₂(3) to establish a more consistent framework, yet two limitations remain. First, its standard update fails to synergize complementary error information within the left-invariant formulation, capping estimation accuracy. Second, velocity and position states converge slowly under extreme misalignment. To address these issues, a multiplicative federated IEKF (MF-IEKF) was proposed. A geometrically consistent state propagation model on SE₂(3) is derived from multiplicative IMU pre-integration. Two parallel, mutually inverse left-invariant error sub-filters (ML₁-IEKF and ML₂-IEKF) cooperate to improve overall accuracy. For large-misalignment scenarios, a short-term multiplicative right-invariant sub-filter is introduced to suppress initial position and velocity errors. Extensive Monte Carlo simulations and KITTI dataset experiments show that MF-IEKF achieves higher navigation accuracy and robustness than ML₁-IEKF. Full article

Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of traditional algorithms on accurate line parameters, this paper introduces a novel fault location algorithm for hybrid transmission lines. The method integrates a data synchronization approach with a physics-informed neural network (PINN) implemented using a backpropagation (BP) neural network architecture. First, the proposed synchronization algorithm corrects the phase misalignment between double-ended recordings. Second, a distributed-parameter fault location model is developed to derive a location function, which is then used to construct physics-informed input features. This approach reduces the need for large fault datasets, addressing the challenge of the low occurrence of faults in practice. Finally, a BP neural network employing these physics-informed features is utilized to learn the nonlinear mapping to the fault location, allowing for accurate fault location, enabling accurate positioning without requiring precise line parameters. Validation using actual line data confirms the high precision of the synchronization algorithm. Furthermore, simulations show that the proposed fault location algorithm achieves high accuracy and remains robust against variations in fault position, type, transition resistance, inception angle, and load current, making it highly practical for real engineering applications. Full article

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

This study addresses the nonlinear estimation problem in the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation by proposing an improved filtering algorithm based on $S{E}_2(3)$ Lie group state representation. A dynamic model satisfying the group affine condition is established to systematically construct both left-invariant and right-invariant error state spaces, upon which two nonlinear filtering approaches are developed. Although the fast unscented transformation method is not novel by itself, its first integration with the $S{E}_2(3)$ Lie group model for SINS/DVL integrated navigation represents a significant advancement. Experimental results demonstrate that under large misalignment angles, the proposed method achieves slightly lower attitude errors compared to linear approaches, while also reducing position estimation errors during dynamic maneuvers. The 12,000 s endurance test confirms the algorithm’s stable long-term performance. Compared with conventional unscented Kalman filter methods, the proposed approach not only reduces computation time by 90% but also achieves real-time processing capability on embedded platforms through optimized sampling strategies and hierarchical state propagation mechanisms. These innovations provide an underwater navigation solution that combines theoretical rigor with engineering practicality, effectively overcoming the computational efficiency and dynamic adaptability limitations of traditional nonlinear filtering methods. Full article

This study explores the critical role of the flow deflector in suppressing boundary layer separation and enhancing aerodynamic efficiency through systematic geometric parameterization and computational analysis. By defining eight key design variables, this research identifies optimal configurations that significantly delay flow separation at high angles of attack. Computational Fluid Dynamics (CFD) simulations reveal that optimized deflector geometries enhance suction peaks near the airfoil leading edge, redirect separated flow toward the upper surface, and inject momentum into the boundary layer to generate a more positive lift coefficient. The numerical results demonstrate that the optimized design achieves a 58.4% increase in lift coefficient and an 83.3% improvement in the lift–drag ratio by effectively mitigating large-scale vortical structures inherent in baseline configurations. Sensitivity analyses further highlight threshold-dependent “sudden-jump” behaviors in lift coefficients for parameters such as element spacing and deflection angles, while thickness exhibits minimal influence. Additionally, pre-stall optimizations show that strategically aligned deflectors preserve baseline performance with a 0.4% lift gain, whereas misaligned configurations degrade aerodynamic efficiency by up to 9.1%. These findings establish a direct correlation between deflector-induced flow redirection and separation suppression, offering actionable insights for passive flow control in stalled regimes. This research advances fundamental understanding of flow deflector-based separation management and provides practical guidelines for enhancing aerodynamic performance in aerospace applications. Full article

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which limit their applicability in high-precision navigation. To address these limitations, this paper proposes an adaptive mixed-order spherical simplex-radial cubature particle filter (MLG-AMSSRCPF) algorithm based on matrix Lie group representation. In this approach, attitude errors are represented on the matrix Lie group SO(3), while velocity errors and inertial sensor biases are retained in Euclidean space. Efficient bidirectional conversion between Euclidean and manifold spaces is achieved through exponential and logarithmic maps, enabling accurate attitude estimation without the need for Jacobian matrices. A hybrid-order cubature transformation is introduced to reduce model linearization errors, thereby enhancing the estimation accuracy. To improve the algorithm’s adaptability in dynamic noise environments, an adaptive noise covariance update mechanism is integrated. Meanwhile, the particle similarity is evaluated using Euclidean distance, allowing the dynamic adjustment of particle numbers to balance the filtering accuracy and computational load. Furthermore, a multivariate Huber loss function is employed to adaptively adjust particle weights, effectively suppressing the influence of outliers and significantly improving the robustness of the filter. Simulation and the experimental results validate the superior performance of the proposed algorithm under moving-base alignment conditions. Compared with the conventional cubature particle filter (CPF), the heading accuracy of the MLG-AMSSRCPF algorithm was improved by 31.29% under measurement outlier interference and by 39.79% under system noise mutation scenarios. In comparison with the unscented Kalman filter (UKF), it yields improvements of 58.51% and 58.82%, respectively. These results demonstrate that the proposed method substantially enhances the filtering accuracy, robustness, and computational efficiency of SINS, confirming its practical value for initial alignment in high-noise, complex dynamic, and nonlinear navigation systems. Full article

Due to the distant acquisition and bird’s-eye perspective of remote sensing images, ground objects are distributed in arbitrary scales and multiple orientations. Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and orientation. However, these methods solve scale and orientation issues separately and ignore their deeper coupling relationships. When the scale features extracted by the network are significantly mismatched with the object, it is difficult for the detection head to effectively capture orientation of object, resulting in misalignment between object and bounding box. Therefore, we propose a one-stage detector—Scale First Refinement-Angle Detection Network (SFRADNet), which aims to fine-tune the rotation angle under precise scale feature matching. We introduce the Group Learning Large Kernel Network (GL²KNet) as the backbone of SFRADNet and employ a Shape-Aware Spatial Feature Extraction Module (SA-SFEM) as the primary component of the detection head. Specifically, within GL²KNet, we construct diverse receptive fields with varying dilation rates to capture features across different spatial coverage ranges. Building on this, we utilize multi-scale features within the layers and apply weighted aggregation based on a Scale Selection Matrix (SSMatrix). The SSMatrix dynamically adjusts the receptive field coverage according to the target size, enabling more refined selection of scale features. Based on precise scale features captured, we first design a Directed Guiding Box (DGBox) within the SA-SFEM, using its shape and position information to supervise the sampling points of the convolution kernels, thereby fitting them to deformations of object. This facilitates the extraction of orientation features near the object region, allowing for accurate refinement of both scale and orientation. Experiments show that our network achieves a mAP of 80.10% on the DOTA-v1.0 dataset, while reducing computational complexity compared to the baseline model. Full article

The attitude accuracy of high-resolution satellite images is the main factor affecting their geometric positioning accuracy. Bundle block adjustment is the main method for realizing the simultaneous estimation of attitude models for overlapping images over a large area. In the current research on the joint positioning of high-resolution multi-line array satellite images, the adjustment is usually carried out with the view or load as a unit without considering the consistency of the error of the same platform. In this paper, we develop a self-calibration strip bundle adjustment scheme that considers the boresight misalignment among multiple cameras. By introducing the installation angle between multiple loads, we fully utilized their geometric constraint relationship with the same platform to establish a unified attitude compensation model for multiple loads. The experimental results of the ZiYuan3 (ZY-3) satellite image show that, when the ground control points (GCPs) are laid only at four corner points of the image, the image plane and elevation accuracies are 1.85 m and 1.87 m after an adjustment using this method, which can achieve comparable accuracies with those obtained by a traditional program based on an adjustment with more GCPs. Full article

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the $\mathbb{SE}\left(3\right)$ group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the $\mathbb{SE}\left(3\right)$ group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker–Campbell–Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the $\mathbb{SE}\left(3\right)$ group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional $\mathbb{SE}\left(3\right)$ group-based model. Full article

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE₂(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE₂(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level. Full article

When the dual rotor system of the aircraft engine is operating, the mass eccentricity of the power turbine rotor and the misalignment of the shaft coupling or the bearing will cause too large vibration of the rotor; this vibration leads to the rub-impact between the rotor and the casing. The power turbine rotor from the dual rotor system is taken as the research object in this paper. Considering the misalignment, the resulting rub-impact faults, the imbalance of rotor and the disk offset, the equation of motion for the system is developed according to the Lagrangian Equation, and then the Range-Kutta Method is adopted to solve the equation. The influence of the key parameters such as the rotating speed, the misalignment angle and the rub-impact clearance on the dynamics of the system is studied; the finite element analysis was carried out to validate the correctness of the theoretical modeling method. The results show that the rub-impact increases the stiffness of the system; the Hopf bifurcation occurs in the misalignment and rub-impact coupling system; the vibrational stability near the half of the switching speed slumps with the increase of the misalignment angle; with increasing of the stiffness, the number of the chaotic zone increases, and the range of the chaos is widening; enlarging the rub-impact clearance is beneficial to reduce the degree of the rub-impact system and enhance the stability of the system. Full article

Spherical roller bearings (SRBs) are widely used under self-aligning operating conditions, such as rotor bending or an angular misalignment between inner and outer rings due to their self-aligning function. However, the characterization of SRBs’ self-aligning function is often ignored in the present models. The reason for this is that the self-aligning condition is essentially a fault condition, and many scholars have assumed SRBs are in an ideal operating condition. Although there is nothing wrong with this analysis theoretically, it is incapable of characterizing SRBs’ service behavior comprehensively. In this work, the Lagrange equation was introduced to model the relationship among the rollers and the inner and outer rings. The contact region in particular was characterized in detail in order to solve the problems of undetermined contact status (UCS) and the varying law of the self-aligning contact angle (SAC angle). For the experiment, a novel SRBs pedestal with a self-aligning operating condition was designed, and the relevant self-aligning function testing was carried out. A good agreement was shown between the theoretical and experimental results. The results pointed out that, if taking no account of the self-aligning function, SRBs can be regarded as angular contact ball bearings or cylinder roller bearings. The amplitude of the inner-ring motion orbit is determined by the external load, but the shape is influenced by the direction and magnitude of the SAC angle. In the example of this paper, the values of the main frequency equal 136.8 Hz. Some additional frequencies are clearly aroused under the self-aligning operating condition, whose value is approximately equal to 8.3 Hz or its integer multiples. The dynamic performance of SRBs will be substantially improved by a light axial load plus an anticlockwise self-aligning contact angle rather than a large axial preload. Full article

Composite materials with a polymer matrix are used on a large scale to make light structures that involve high responsibility. The failure mechanisms of composite materials are very complex and for this reason, advanced techniques for damage detection and the assessment of structural integrity are required. The continuous structural health monitoring (SHM) uses nondestructive testing (NDT) techniques, sensors integrated into the structures, computers and dedicated software. This article presents a new automatic and precise method for detecting the orientation of strain gauges glued onto composite materials with a polymer matrix. The automatic identification of both the directions of the reinforcing fibers and that of the orientation of the strain gauge, respectively, allows for the calculation of the angle between these two directions. By knowing the difference between the nominal value of this angle and the value actually obtained after gluing the strain gauge, corrections obtained by calculation on the experimental values can be applied, using equations found in specialized literature. In this way, a drastic reduction of measurement errors introduced by the misalignment of strain gauges glued on composite materials can be achieved, resulting in a significant increase of measurement accuracy, which contributes to increasing the security of the monitored structures. Full article

In this paper, we propose a misalignment correct method and a particle detection algorithm to improve the accuracy in the quality inspection of the LCD module after the anisotropic conductive film (ACF) bonding. We use only one camera to acquire images of multiple positions in order to establish the transformation from the image space to the world coordinate. Our method can accurately determine the center of rotation of the carrier table and calculate the deviation of position and angle of the tested module. Compared to traditional ways that rely on multiple cameras to align the large-sized product, our method has the advantages of simple structure, low cost, and fast calibration process. The particle detection is performed after positioning all bumps of the bonded module. The gray morphology-based algorithm is developed to detect the extreme point of every particle and refine the particle result through blob analysis. This method reduces the over-checking rate and performs better on the detection precision for dense particles. We verify the effectiveness of our proposed methods in our experiments. The alignment error can be less than 0.05 mm, and the accuracy of the particle detection is 93% while the recall rate is 92.4%. Full article