Search Results (132)

This paper considers a numerically stable discrete-time representation of the three-dimensional Constant Turn (CT) motion model within the Interacting Multiple Model (IMM) framework for radar tracking of maneuvering aerial targets. Classical discrete CT models used in Kalman-filter-based tracking contain singular expressions in the vicinity of zero and near-zero turn rates, which may degrade estimation accuracy and impair numerical robustness. To address this problem, a Maclaurin-series-based discretization of the three-dimensional CT model is developed, in which the state transition matrix and the process-noise-related matrices are approximated in polynomial form. Linear, quadratic, and cubic approximations are constructed and analyzed. The proposed CT model is integrated into a three-model IMM algorithm together with the Constant Velocity (CV) and Constant Acceleration (CA) models. The study includes both an internal comparison of Maclaurin approximations of different orders and an external comparison with the classical CT discretization and a Padé-based reference discretization. Numerical experiments are performed for representative three-dimensional maneuvering scenarios under radar measurement conditions. The obtained results show that the proposed discretization eliminates singular behavior near zero turn rate while preserving the tracking capability of the IMM estimator. The comparative analysis demonstrates that the quadratic Maclaurin approximation provides the most favorable trade-off between modeling accuracy, numerical stability, and computational cost. It yields tracking performance close to higher-order approximations and competitive with the Padé-based reference approach, while remaining simpler for practical implementation in real-time radar tracking systems. These results indicate that the proposed quadratic approximation is a suitable solution for maneuvering aerial target tracking in three-dimensional radar applications. Full article

Cyclone separators remain widely used for gas–solid separation, yet analytical prediction of cut size and pressure drop remains challenging. This study presents an explicit semi-empirical model for the cut size (d₅₀) of reverse-flow cyclones based on the radial particle equation of motion in cylindrical coordinates, with d₅₀ obtained by equating radial migration time and residence time. A closed-form solution is derived in the Stokes regime, whereas non-Stokes behavior is handled numerically through the Schiller–Naumann drag correction. Turbulence is incorporated through a phenomenological correction, and the grade–efficiency curve is represented by a logistic relation. The model was implemented in MATLAB R2025a and applied in a parametric study covering inlet velocity, particle density, cyclone diameter, and gas viscosity. A Euler-type pressure drop relation was included to examine the separation–energy trade-off. Validation on the Kim et al. benchmark using one calibration point per cyclone family and six independent verification cases yielded a mean absolute percentage error of 13.5% and a root mean square error of 0.22 μm for d₅₀; the paired pressure drop check gave a 2.8% mean absolute percentage error. A complementary benchmark based on Wang et al. using 15 cm 1D3D and 2D2D cyclones under actual-air and standard-air conditions further supported the family-calibrated use of the model. A separate scale-up test showed that constant swirl intensity similarity is not transferable across large diameter changes. The formulation provides a transparent reduced-order tool for preliminary design and sensitivity analysis. Full article

To monitor online the operational condition and quality of a desktop fused deposition modeling (FDM) printer, the dynamics of vibro-acoustics must be accurately understood. In this paper, an electromechanical coupling (EMT) framework is established to relate the dynamics of stepper actuation, the transmission chain, and machine motion, deriving a steady-state frequency–velocity mapping for steady or near steady printing segments. The mapping is evaluated by numerical calculation to obtain a theoretical drive frequency for different toolpath directions and commanded printing velocities. Validation is performed on the experiment platform I. Drive-side vibration is measured by an accelerometer mounted on the x-axis beam near the motor end. An acoustic channel is recorded as an auxiliary qualitative cross-check rather than for quantitative error evaluation. For steady printing segments, the dominant frequency in drive-side vibration is compared with the theoretical drive frequency. In the tested steady segments and toolpath directions, the relative error remained below 3%. In a further case study, the G-code is modified to introduce two constant printing velocity segments (40 mm/s and 80 mm/s) within the same continuous record, enabling a direct comparison of dominant frequencies between two steady segments. The results show that, under open-loop stepper drive and within the steady/near steady scope adopted here, a drive-related dominant frequency can be observed stably in the x-axis beam vibration response and matches the theoretical drive frequency. When the commanded constant printing velocity is doubled, the dominant frequency in drive-side vibration in the corresponding steady segment changes by approximately a proportional factor. This study provides a verifiable drive referenced frequency–velocity mapping for steady segments under the tested configuration and a traceable frequency reference for steady segment comparisons within the same print record in subsequent case studies. Full article

This paper presents a numerical investigation of vortex-induced vibration (VIV) of six elastically mounted circular cylinders in oscillatory flow, three smooth and three biofouled with triangular surface roughness elements. The study aims to characterise the influence of the longitudinal spacing ratio ($L/D=3,4,$ and $5$) on the two-degree-of-freedom (2DOF) vibration response at a constant Keulegan–Carpenter number of $KC=10$. Simulations are performed using the transient RANS equations with the SST $k–\omega$ turbulence model, and structural motion is resolved using a dynamic mesh approach. Lock-in behaviour is observed over the reduced velocity range $5\le U_r\le 10$. Biofouled cylinders generally exhibit higher in-line displacement amplitudes than smooth cylinders in the initial and lower lock-in branches, whereas smooth cylinders tend to attain higher in-line amplitudes in the upper lock-in branch. The spacing ratio $L/D$ is found to significantly influence the response, with peak vibration amplitudes varying non-uniformly across the array and no single spacing configuration being optimal for all cylinders. This behaviour is further supported by analyses of trajectories, frequency content, and vorticity fields. Among the smooth cylinders, the middle cylinder exhibits the largest in-line displacement amplitude of $3.28D$ at $L/D=5$ and the largest cross-flow displacement of $1.34D$ at $L/D=3$. For the biofouled configurations, the middle and upstream cylinders show the highest in-line displacement amplitude of $2.69D$ at $L/D=4$, while the maximum cross-flow displacement of $1.27D$ is observed for the upstream cylinder at $L/D=5$. Full article

Underwater multi-sensor passive tracking faces two critical challenges: the strong nonlinearity of Doppler–bearing measurements and underwater acoustic propagation delays. To address these issues, this paper proposes a distributed fusion filtering method based on extended measurement space modeling and delay compensation. First, an extended measurement space comprising range, Doppler frequency, bearing, and bearing rate is constructed to transform the nonlinear measurements into a linear framework. Within this space, linear prediction equations for constant velocity (CV) motion are derived to facilitate linearized local filtering. Furthermore, a closed-form linear solution for propagation delay is established within the constructed state space. To resolve the incompatibility of multi-node estimates caused by local coordinate frame discrepancies, a distributed architecture based on the Unscented Transform (UT) is designed. In this architecture, local states are transformed into a unified Cartesian coordinate system for temporal compensation and fast Covariance Intersection (FCI) fusion, followed by an inverse mapping back to the local space. Simulation results demonstrate that, compared with traditional nonlinear methods based on mixed coordinate systems, the proposed method significantly reduces nonlinear approximation errors, thereby enhancing tracking accuracy and robustness. Full article

Obstacle-avoidance risk threshold control and global discrete keypoint re-entry are critical factors influencing the smooth dynamic obstacle avoidance of unmanned vessels. For underactuated USVs, which operate in planar motion with three degrees of freedom (surge, sway, and yaw) but only two independent control inputs (surge velocity and yaw rate), this paper designs a layered obstacle-avoidance strategy featuring adaptive global path re-entry points, combined with short- and long-term obstacle trajectory prediction and risk perception. This method employs an Interactive Multiple Model (IMM) integrating Constant Velocity (CV), Constant Acceleration (CA), and Constant Turn Rate and Acceleration (CTRA) models to perform long-term spatiotemporal trajectory prediction for dynamic obstacles, constructing a spatiotemporal risk cost map. Long-term dynamic obstacle-avoidance trajectory planning is achieved through optimized adaptive global trajectory re-entry points and an improved A* algorithm. This long-term avoidance trajectory replaces the global path from the avoidance start to the re-entry point, providing a smooth, continuous long-term avoidance prediction. To ensure real-time collision avoidance effectiveness, an improved Dynamic Window Approach (DWA) algorithm uses the long-term avoidance trajectory as a foundation. It integrates the IMM’s short-term spatiotemporal obstacle trajectory prediction, sampling in the velocity and steering angle space to generate short-term avoidance control commands. Finally, the long-term and short-term obstacle-avoidance planning are executed in a receding-horizon manner, where the local DWA planner updates control inputs over a short rolling window without solving a full constrained optimization problem. This establishes a hierarchical avoidance strategy: long-term prediction enables smooth avoidance, while short-term prediction enables real-time avoidance, ensuring the continuity and timeliness of dynamic obstacle avoidance. Simulation results demonstrate that compared with traditional A* planning, the proposed risk-aware A* reduces cumulative collision risk by 62% and increases the minimum obstacle clearance distance by over 32.1%, while maintaining acceptable path length growth. This approach effectively reduces collision risks during navigation, enhances path smoothness, and improves navigation safety. Full article

This paper investigates the degradation of detection performance in FDA–MIMO radar systems caused by signal mismatch under constant-velocity target motion and develops a robust detection strategy to mitigate this effect. Under the effective hypothesis, a stochastic term is introduced into the received radar signal to account for mismatch uncertainty. This term is modeled as a Gaussian random variable whose covariance structure is identical to that of the noise while being scaled by an unknown robustness parameter. Based on the resulting statistical model, three robust detectors are derived using the One-Step Generalized Likelihood Ratio Test (OGLRT), the Two-Step GLRT (TGLRT), and the Gradient test. Simulation results demonstrate that all proposed detectors preserve the Constant False Alarm Rate (CFAR) property under the null hypothesis. Further performance evaluations reveal that, in the absence of signal mismatch, the OGLRT and Gradient detectors provide superior detection performance, whereas under mismatched conditions, all three detectors exhibit improved robustness. These findings provide both theoretical insight and practical guidance for the design and implementation of FDA–MIMO radar systems, contributing to the enhancement and optimization of detection performance in realistic operating environments. Full article

As a critical component for motion control in heavy machinery, high-speed hydraulic actuators require effective buffering to mitigate end-impact. This paper proposes a compact buffering cylinder with a multi-orifice sleeve. A theoretical model was established to derive the throttling area profile for constant deceleration. A detailed numerical simulation model was then developed, and the key orifice parameters (diameter and spacing) were optimized using a Particle Swarm Optimization (PSO) algorithm to maximize buffering efficiency and smoothness. A prototype based on the optimal design was manufactured and tested dynamically. Experimental results demonstrate that the buffer smoothly arrested a piston with an initial velocity of 8 m/s and a moving mass of 80 kg within a 250 mm stroke. The optimized design achieved a 14% increase in buffering efficiency and reductions in peak force and pressure compared to the initial design, validating the proposed optimization methodology and providing a reliable solution for high-speed actuator protection. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

Multi-radar systems can significantly improve tracking robustness and accuracy, but practical deployments are challenged by asynchronous sensing timestamps across distributed platforms and by limited communication bandwidth. This paper proposes a communication-efficient asynchronous track fusion framework based on state and covariance projection. Each radar performs local Kalman filtering and transmits only a compact track message consisting of the posterior state estimate, the associated error covariance, and a timestamp. At the fusion center, a causal reference time is chosen as the latest received timestamp, and all tracks are projected to this common time using a hybrid constant-acceleration (CA)/constant-velocity (CV) motion model with appropriately discretized process noise, followed by information-form (inverse-covariance) fusion. Under standard linear-Gaussian assumptions, the fusion rule is minimum mean square error (MMSE)-optimal when the projected estimation errors are approximately independent. We also analyze the computational complexity and the communication payload of the proposed procedure. Monte Carlo simulations with five heterogeneous radars and random inter-radar time offsets up to 37.5 ms over 100 runs show that the proposed fusion reduces the steady-state range root mean square error (RMSE) by about 66% and the radial-velocity RMSE by about 31% relative to the average single-radar tracker, while maintaining statistical consistency as verified by the normalized estimation error squared (NEES). These results indicate that projection-based track fusion provides an effective accuracy–communication trade-off for asynchronous multi-radar tracking. Full article

With the rapid development of mobile robotics, wheeled bipedal robots, which combine the terrain adaptability of legged robots with the high mobility of wheeled systems, have attracted increasing research attention. To address the balance control problem during both standing and locomotion while reducing the influence of noise on control performance, this paper proposes a balance control framework based on a Linear Quadratic Regulator integrated with an Extended Kalman Filter (KLQR). Specifically, a baseline LQR controller is designed using the robot’s dynamic model, where the control input is generated in the form of wheel-hub motor torques. To mitigate measurement noise and suppress oscillatory behavior, an Extended Kalman Filter is applied to smooth the LQR torque output, which is then used as the final control command. Filtering experiments demonstrate that, compared with median filtering and other baseline methods, the proposed EKF-based approach significantly reduces high-frequency torque fluctuations. In particular, the peak-to-peak torque variation is reduced by more than 60%, and large-amplitude torque spikes observed in the baseline LQR controller are effectively eliminated, resulting in continuous and smooth torque output. Static balance experiments show that the proposed KLQR algorithm reduces the pitch-angle oscillation amplitude from approximately ±0.03 rad to ±0.01 rad, corresponding to an oscillation reduction of about threefold. The estimated RMS value of the pitch angle is reduced from approximately 0.010 rad to 0.003 rad, indicating improved convergence and steady-state stability. Furthermore, experiments involving constant-speed straight-line locomotion and turning indicate that the KLQR algorithm maintains stable motion with velocity fluctuations limited to within ±0.05 m/s. The lateral displacement deviation during locomotion remains below 0.02 m, and no abrupt acceleration or deceleration is observed throughout the experiments. Overall, the results demonstrate that applying Extended Kalman filtering to smooth the control torque effectively improves the smoothness and stability of LQR-based balance control for wheeled bipedal robots. Full article

Maneuvering targets exhibit range migration (RM) and Doppler-frequency migration (DFM) during the coherent integration period. Most existing coherent integration methods model maneuvering target motion with a single motion mode. However, highly maneuvering targets often undergo mode-switching, which degrades the detection performance of conventional algorithms. To address this problem, this paper proposes a model–data dual-driven method for mode-switching radar targets. From the model-driven perspective, the range evolution over time is derived in the Cartesian coordinate system for transitions among constant-velocity (CV), constant-acceleration (CA), and constant-turn (CT) motions, thereby constructing multiple possible mode-switching scenarios. Subsequently, from the data-driven perspective, a hierarchical residual network and keypoint loss functions are designed to learn and capture the uncertainty associated with mode-switching, thereby accurately inferring the initial and switching points of the target. Furthermore, to enhance the interpretability of the network, probability heatmap visualization is employed to intuitively reveal the internal mechanisms of the network. Finally, by partitioning the Coherent Processing Interval (CPI) based on network-detected keypoints, the proposed method performs efficient piecewise coherent integration for different motion models by integrating along the slow-time echo-envelope migration path. Simulation results demonstrate that the proposed method not only effectively eliminates both RM and DFM but also achieves strong detection performance and favorable computational efficiency. Full article

Multi-fish tracking and behavior analysis in deep-sea cages face two critical challenges: first, the homogeneity of fish appearance and low image quality render appearance-based association unreliable; second, standard linear motion models fail to capture the complex, nonlinear swimming patterns (e.g., turning) of fish, leading to frequent identity switches and fragmented trajectories. To address these challenges, we propose SOD-SORT, which integrates a Constant Turn-Rate and Velocity (CTRV) motion model within an Extended Kalman Filter (EKF) framework into DeepOCSORT, a recent observation-centric tracker. Through systematic Bayesian optimization of the EKF process noise (Q), observation noise (R), and ReID weighting parameters, we achieve harmonious integration of advanced motion modeling with appearance features. Evaluations on the DeepBlueI validation set show that SOD-SORT attains IDF1 = 0.829 and reduces identity switches by 13% (93 vs. 107) compared to the DeepOCSORT baseline, while maintaining comparable MOTA (0.737). Controlled ablation studies reveal that naive integration of CTRV-EKF with default parameters degrades performance substantially (IDs: 172 vs. 107 baseline), but careful parameter optimization resolves this motion-appearance conflict. Furthermore, we introduce a statistical quantization method that converts variable-length trajectories into fixed-length feature vectors, enabling effective unsupervised classification of normal and abnormal swimming behaviors in both the Fish4Knowledge coral reef dataset and real-world Deep Blue I cage videos. The proposed approach demonstrates that principled integration of advanced motion models with appearance cues, combined with high-quality continuous trajectories, can support reliable behavior modeling for aquaculture monitoring applications. Full article

The start-up of electrophoretic motion in a suspension of uniformly charged, porous, spherical particles within an arbitrary electrolyte solution under a suddenly applied electric field is investigated. The unsteady Stokes/Brinkman equations, modified to include the electric body force, are solved for the fluid velocity field using a unit cell model to account for the particle-particle interactions. An explicit expression for the transient electrophoretic velocity of a porous particle in a unit cell is derived in the Laplace transform domain as a function of the key governing parameters. The transient electrophoretic velocity, when normalized by its steady-state counterpart, increases monotonically with both elapsed time and the ratio of particle radius to Debye length, with other parameters held constant. It generally increases with the ratio of particle radius to permeation length and with porosity, while decreasing monotonically with an increase in the particle-to-fluid density ratio. Similar to its steady-state value, the transient electrophoretic mobility of the suspension is typically a decreasing function of the particle volume fraction. However, under conditions of small elapsed time and large density ratio, the transient mobility may exhibit an initial increase with particle volume fraction. Full article

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article