Search Results (64)

To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters. Full article

The increasing congestion of low Earth orbit (LEO) has raised the need for efficient collision avoidance strategies, especially for CubeSats without propulsion systems. This study proposes a methodology for evaluating passive collision avoidance maneuvers using aerodynamic control via a satellite’s Attitude Determination and Control System (ADCS). By adjusting orientation, the satellite modifies its exposed surface area, altering atmospheric drag and lift forces to shift its orbit. This new approach integrates atmospheric modeling (NRLMSISE-00), aerodynamic coefficient estimation using the ADBSat panel method, and orbital simulations in Systems Tool Kit (STK). The LUME-1 CubeSat mission is used as a reference case, with simulations at three altitudes (500, 460, and 420 km). Results show that attitude-induced drag modulation can generate significant orbital displacements—measured by Horizontal and Vertical Distance Differences (HDD and VDD)—sufficient to reduce collision risk. Compared to constant-drag models, the panel method offers more accurate, orientation-dependent predictions. While lift forces are minor, their inclusion enhances modeling fidelity. This methodology supports the development of low-resource, autonomous collision avoidance systems for future CubeSat missions, particularly in remote sensing applications where orbital precision is essential. Full article

This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances. Full article

This study addresses the gap in the experimental validation of the tilt-rotor vertical take-off and landing (VTOL) UAVs by developing a novel prototype that integrates fixed-wing and multi-rotor advantages. A dynamic model based on the “X” quadrotor configuration was established, and Euler parameters were employed to derive the attitude transformation matrix. Structural optimization using hybrid meshing and inertia release methods revealed a maximum deformation of 57.1 mm (2.82% of half-wingspan) and stress concentrations below material limits (379.21 MPa on fasteners). The landing gear was optimized using the unified objective method, and the stress was reduced by 32.63 MPa compared to the pre-optimization stress. Vibration analysis identified hazardous frequencies (11–12 Hz) to avoid resonance. Stable motor speed tracking (±5 RPM) and rolling attitude control (less than 10% error) are achieved using a dual-serial PID control system based on the DSP28377D master. Experimental validation in low-altitude flights confirmed the prototype’s feasibility, though ground effects impacted pitch/yaw performance. This work provides critical experimental data for future tilt-rotor UAV development. Full article

Remote sensing visual-light spectral (VIS) maps provide stable and rich features for geo-localization. However, it still remains a challenge to make use of VIS map features as localization references at night. To construct a cross-day-and-night localization system for long-duration UAVs, this study proposes a visual–inertial integrated localization system, where the visual component can register both RGB and infrared camera images in one unified VIS map. To deal with the large differences between visible and thermal images, we inspected various visual features and utilized a pre-trained network for cross-domain feature extraction and matching. To obtain an accurate position from visual geo-localization, we demonstrate a localization error compensation algorithm with considerations about the camera attitude, flight height, and terrain height. Finally, the inertial and dual-vision information is fused with a State Transformation Extended Kalman Filter (ST-EKF) to generate long-term, drift-free localization performance. Finally, we conducted actual long-duration flight experiments with altitudes ranging from 700 to 2400 m and flight distances longer than 344.6 km. The experimental results demonstrate that the proposed method’s localization error is less than 50 m in its RMSE. Full article

In high-dynamic battlefield environments, anti-ship missiles must perform intricate attitude adjustments and energy management within time constraints to hit a target accurately. Traditional optimization methods face challenges due to the high speed, flexibility, and varied constraints inherent to anti-ship missiles. To overcome these challenges, this research introduces a three-dimensional (3D) multi-stage trajectory optimization approach based on the hybrid multi-objective particle swarm optimization algorithm (MOPSO-h). A multi-stage optimization model is developed for terminal trajectory, dividing the flight process into three stages: cruising, altitude adjustment, and penetration dive. Dynamic equations are formulated for each stage, incorporating real-time observations and overload constraints and ensuring the trajectory remains smooth, continuous, and compliant with physical limitations. The proposed algorithm integrates an adaptive hybrid mutation strategy, effectively balancing global search with local exploitation, thus preventing premature convergence. The simulation results demonstrate that, in typical scenarios, the mean miss distance optimized by MOPSO-h remains no greater than 2.34 m, while the terminal landing angle is consistently no less than 85.68°. Furthermore, MOPSO-h enables the missile’s cruise altitude and speed, driven by multiple models, to maintain long-term stability, ensuring that the maneuver overload adheres to physical constraints. This research provides a rigorous and practical solution for anti-ship missile trajectory design and engagement with shipborne air defense systems in high-dynamic environments, achieved through a multi-stage collaborative optimization mechanism and error analysis. Full article

This study addresses the problem of attitude and altitude tracking for a quadrotor system in the presence of parameter uncertainties. The goal is to develop a robust control strategy that can handle the nonlinear, strongly coupled dynamics of the quadrotor. To achieve this, we propose a fractional-order sliding mode control (FOSMC) scheme, which is specifically designed to improve system performance under uncertain parameters. The FOSMC approach is combined with additional adaptive laws to further enhance the robustness of the control system. We derive the necessary control laws and apply them to the quadrotor’s state-space representation, ensuring that the system remains stable and performs accurately in the presence of uncertainties. Numerical simulations are conducted to evaluate the effectiveness of the proposed control strategy. The results show that the FOSMC-based controller successfully achieves precise tracking of both attitude and altitude, demonstrating significant robustness against parameter variations and disturbances. In conclusion, the proposed FOSMC scheme provides a reliable solution for controlling quadrotor systems in uncertain environments, offering the potential for real-world applications in autonomous UAV operations. Full article

Coaxial drones have garnered popularity owing to their energy efficiency and compact design. However, the precise navigation of these drones in complex and dynamic flight scenarios is limited by inaccuracies in heading/yaw estimation. Conventional heading estimation methods rely on magnetometers and real-time kinematic Global Navigation Satellite Systems (RTK-GNSS), which directly measure heading angle. However, the small size of microdrones restricts the placement of magnetometers away from magnetic interference and prevents the use of directional antennas. Moreover, single-antenna alignment algorithms are highly susceptible to errors caused by nonlinearity, leading to significant inaccuracies in heading estimation. To address these challenges, this paper proposes a hybrid heading estimation approach that integrates Motion-Adaptive Stabilization with an Angle-Parameterized Extended Kalman Filter (APEKF). This method utilizes low-cost GNSS, a magnetometer, and an Inertial Measurement Unit (IMU). Heading is initialized based on the drone’s static attitude, with an adaptive threshold established during takeoff to account for varying flight conditions. As the drone reaches higher altitudes, heading estimation is further stabilized. GNSS velocity observations enhance estimation accuracy through horizontal maneuvering alignment achieved by incorporating multiple sub-filter techniques and residual-based fusion. In the simulations and onboard experiments in this study, the proposed heading estimation method demonstrated a precision of approximately 1.01° post-takeoff, with the alignment speed enhanced by 43%. Moreover, the method outperformed existing estimation techniques and, owing to its low computational overhead, can serve as a reliable full-stage backup across various drone applications. Full article

A skid-to-turn (STT) missile is an axisymmetric structure missile, and its control system consists of a pitch channel and a yaw channel with an axisymmetric structure. To achieve the fast response of the STT missile system, a compound control method of aerodynamic force and lateral thrust based on regional pole assignment (RPA) is proposed. In the aerodynamic control system, the linear quadratic regulator (LQR) is used to design the controller, and the sliding mode control method is used to design the controller of the lateral thrust system. The regional pole assignment is introduced into the aerodynamic system to improve the compound control system response speed. The problems of regional pole assignment and system stability are solved by a linear matrix inequality (LMI). Considering that the missile flies at different altitudes, the missile system is controlled by gain scheduling. Compared to previous designs of time-varying compound control systems for STT missiles or hypersonic vehicles, in order to meet the practical requirement of a fast response for the vehicle, this time-varying compound control strategy can achieve faster tracking response and attitude control for the STT missile. Finally, through simulations of the pitch channel and yaw channel control systems of the STT missile, the effectiveness of the designed compound control system in achieving a fast response is verified. Full article

This paper presents the simulation and controller optimization of a quadrotor Unmanned Aerial Vehicle (UAV) system. The quadrotor model is derived adopting the Newton-Euler approach, and is intended to be constituted by four three-phase Permanent Magnet Synchronous Motors (PMSM) controlled with a velocity control loop-based Field Oriented Control (FOC) technique. The Particle Swarm Optimization (PSO) algorithm is used to tune the parameters of the PID controllers of quadrotor height, quadrotor attitude angles, and PMSMs’ rotational speeds, which represent the eight critical parameters of the PMSM-quadrotor UAV system. The PSO algorithm is designed to optimize eight Square Error (SE) cost functions which quantify the error dynamics of the controlled variables. For each stabilization task, the PID tuning is divided in two phases. Firstly, the PSO optimizes the error dynamics of altitude and attitude angles of the quadrotor UAV. Secondly, the desired steady-state rotational speeds of the PMSMs are derived, and the PSO is used to optimize the motors’ dynamics. Finally, the complete PMSM-Quadrotor UAV system is simulated for stabilization during the target task. The study is carried out by means of simulations in MATLAB/Simulink^®. Full article

To address the deficiency of aircraft point cloud training data for low-altitude environment perception systems, a method termed APCG (aircraft point cloud generation) is proposed. APCG can generate aircraft point cloud data in the single photon counting Lidar (SPC-Lidar) system based on information such as aircraft type, position, and attitude. The core of APCG is the aircraft depth image generator, which is obtained through adversarial training of an improved conditional generative adversarial network (cGAN). The training data of the improved cGAN are composed of aircraft depth images formed by spatial sampling and transformation of fine point clouds of 76 types of aircraft and 4 types of drone. The experimental results demonstrate that APCG is capable of efficiently generating diverse aircraft point clouds that reflect the acquisition characteristics of the SPC-Lidar system. The generated point clouds exhibit high similarity to the standard point clouds. Furthermore, APCG shows robust adaptability and stability in response to the variation in aircraft slant range. Full article

A short takeoff and vertical landing unmanned aerial vehicle (STOVL UAV) is significantly influenced by factors such as the ship’s surface effect, deck motion, and jet effect during vertical landing on an aircraft carrier. The existing control logic cannot effectively solve the coupling problem of longitudinal attitude and trajectory, so it is hard to guarantee the stability and control accuracy of the UAV at low speed. To address the aforementioned interference and coupling problems, a comprehensive control law based on a radial basis function neural network (RBFNN) and nonlinear dynamic optimal allocation is designed in this paper. Firstly, the integrated landing control law of the STOVL UAV is designed. Considering the model uncertainty and complex landing environment, an RBFNN is used for online observation and compensation to improve the robustness of the system. Subsequently, a dynamic control allocation module based on nonlinear optimization is developed to simultaneously satisfy force and moment commands. The simulation results show that the integrated control method effectively decouples the pitch attitude and longitudinal trajectory at low speeds, resulting in effective convergence control of pitch angle, forward flight speed, and altitude. The integration of the RBFNN, as evaluated by the integral of absolute error (IAE), results in a 93% improvement in control accuracy compared to the integrated landing control law designed in this paper without the RBFNN integration. Full article

The increasing complexity of aerial acrobatics missions necessitates ever-higher levels of attitude control precision in fixed-wing unmanned aerial vehicles (UAVs). Traditional control methods, such as feedback linearization and small disturbance derivation linear models, falter in maintaining attitude tracking accuracy, due to the presence of unanticipated disturbances—most notably, wave disturbances during low-altitude maritime flights—and model uncertainties introduced by factors like large-angle maneuvers, intricate aerodynamic characteristics, and fuel consumption. Consequently, these limitations impede the successful execution of intricate maneuvers, such as looping, the split-S, the Immelmann turn, and the Pougatcheff cobra maneuver. In response to these challenges, we propose an integral sliding mode control based on disturbance observer (ISMC-DO) system to achieve robust attitude angle tracking amidst model uncertainties and mitigate the effects of wave disturbances. Additionally, quaternion representations are adopted as a supplement to Euler angles, thereby resolving the singularity issues inherent in the latter. By using the Lyapunov function, the ISMC-DO-based control system is shown to be asymptotically stable. Simulation results further validate that ISMC-DO can achieve high-precision attitude tracking control of the UAV under wave disturbance. Full article

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

In the optical satellite on-orbit imaging quality estimation system, the calculation of Modulation Transfer Function (MTF) is not fully standardized, and the influence of atmosphere is often simplified, making it difficult to obtain completely consistent on-orbit MTF measurements and comparisons. This study investigates the effects of various factors—such as edge angle, edge detection methods, oversampling rate, and interpolation techniques—on the accuracy of MTF calculations in the commonly used slanted-edge method for on-orbit MTF assessment, informed by simulation experiments. A relatively optimal MTF calculation process is proposed, which employs the Gaussian fitting method for edge detection, the adaptive oversampling rate, and the Lanczos (a = 3) interpolation method, minimizing the absolute deviation in the MTF results. A method to quantitatively analyze the atmospheric scattering and absorption MTF is proposed that employs a radiative transfer model. Based on the edge images of GF-2 satellite, images with various atmospheric conditions and imaging parameters are simulated, and their atmospheric scattering and absorption MTF is obtained through comparing the MTFs of the ground and top atmosphere radiance. The findings reveal that aerosol optical depth (AOD), viewing zenith angle (VZA), and altitude (ALT) are the primary factors influencing the accuracy of GF-2 satellite on-orbit MTF measurements in complex scenarios. The on-orbit MTF decreases with the increase in AOD and VZA and increases with the increase in ALT. Furthermore, a collaborative analysis of the main influencing factors of atmospheric scattering and absorption MTF indicates that, taking the PAN band of the GF-2 satellite as an example, the atmospheric MTF values are consistently below 0.7905. Among these, 90% of the data are less than 0.7520, with corresponding AOD conditions ranging from 0 to 0.08, a VZA ranging from 0 to 50°, and an ALT ranging from 0 to 5 km. The results can provide directional guidance for the selection of meteorological conditions, satellite attitude, and geographical location during satellite on-orbit testing, thereby enhancing the ability to accurately measure satellite MTF. Full article