Abstracts:A compact single-phase switched-capacitor multilevel inverter (SCMLI) for critical aerospace power applications is presented in this correspondence. The proposed nine-level configuration has a low component count with a single-input dc source, two capacitors, nine switches, and one diode, and has a capacitor self-balancing feature. An selective harmonic elimination pulse width modulation (SHEPWM) strategy is improved to minimize dominant harmonics and to achieve a total harmonic distortion (THD) of ∼8% for the nine-level unit, par with state-of-the-art compact SCMLIs. Three such 9-level units are cascaded to realise a 25-level SCMLI that meets the stringent aerospace limits of THD. Demonstration of THD 1.6% under normal operations and 3.6% under worst-case fault conditions during experimental validation establishes that the proposed modular structure fully complies with MIL-STD-704F and RTCA DO-160G aerospace standards. Furthermore, a balanced operation and high power quality of the designed structure in hardware tests at aircraft’s operating frequency of 400 Hz confirm its efficacy as a compact, fault-tolerant, and standards-compliant inverter topology for aerospace power systems.

Abstracts:Magnetic energy harvester clamped on high-ac-currents-carrying conductor (e.g., split phase of an overhead power line) may serve as a part of an uninhabited aerial electrical vehicle battery charging platform. The brief reveals analytical expression for the power delivered to a constant voltage type load (e.g., battery or regulated dc bus) by an active magnetic energy harvester (AMEH) operated under transfer-window-alignment control for determining the optimal (in terms of load power maximization) number of secondary winding turns (N). It has been recently demonstrated that for a given core, the power harvested by an AMEH from a current-carrying conductor increases monotonically when N is reduced. On the other hand, it is discovered that AMEH conversion losses are concurrently increased. As a result, the power supplied by an AMEH to the load is a nonmonotonic function of secondary winding turns number, attaining a global maxima for certain unique value of N. Analytical expression allowing to obtain the optimal secondary winding turns number is developed in the letter and validated experimentally by application to an AMEH clamped on 200 A, 50 Hz current carrying conductor while driving a 45 V constant voltage load (emulating 12s Li-Ion battery).

Abstracts:Distributed array radar (DAR) can improve the main-lobe jamming suppression ability by expanding the aperture of monostatic radar. However, traditional beamforming methods cannot accurately match the range parameter of steering vector and results in a performance degradation in the near-field applications of DAR. To address this issue, this article proposes a near-field beamforming method for DAR based on nonuniform range segmentation and steering vector cone constraint. First, the proposed method divides the received signal into nonuniform range segments based on the criterion of maximum phase difference between subarrays. The nonuniform range segmentation reduces the near-field steering vector error within each range segment and thus prevents beam pattern distortion. Second, the method constructs a cone constraint that synchronously constrains the main lobe and the range segment. Eigenvectors that meet the constraints are blocked by the eigenprojection matrix preprocessing beamforming. By employing the cone constraint, the main lobe within range segments can be maintained, and residual jamming issues in certain range segments are avoided. Simulation and experimental results show that the proposed main-lobe jamming suppression beamforming method offers superior performance and lower hyperparameter dependence.

Abstracts:Accurate target detection amid noise and clutter remains a fundamental challenge in radar signal processing, particularly for applications, such as autonomous navigation and defense systems. The presence of noise and clutter significantly distorts the reflected radar signals, making reliable detection more difficult. Recent developments in deep learning—especially the use of convolutional neural networks and transfer learning—have shown potential in improving radar signal classification performance. This study investigates the application of ResNet architectures for detecting multiple targets and evaluates their effectiveness against classical constant false alarm rate techniques and adaptive normalized matched filters under conditions of correlated non-Gaussian clutter and low signal-to-clutter-plus-noise ratios. By leveraging pretrained models with strong nonlinear feature extraction capabilities, the proposed approach demonstrates superior detection probability, outperforming both traditional and current state-of-the-art methods in radar signal processing tasks.

Abstracts:In this article, a target defense game involving one Defender and two Attackers is investigated. The Defender, constrained within a specified range, strives to capture as many Attackers as possible by aligning itself with them before they reach the unit circle target, while the Attackers aim to reach the target without being captured. Different from the information structure setups in existing literature, this article introduces an asymmetry in information: the maximum speed of the Attackers, which is publicly known to lie within a continuous range, is not revealed to the Defender. To explore whether deception can be implemented by the Attackers through leveraging their information advantage, a critical speed analysis framework is developed to resolve the challenge of infinite possible speeds available to the Attackers. This framework establishes the sufficient and necessary condition for a mismatching direction scenario, in which no Defender strategy can guarantee an optimal payoff across all possible Attacker speeds. Furthermore, our analysis identifies specific dilemma conditions that compel the Defender to make uncertain guesses. Notably, it is demonstrated that by adopting a proposed slow-speed, information-limiting strategy, the Attackers can mislead the Defender into making incorrect decisions, thereby achieving a better payoff compared to employing a maximum capability strategy under complete information. The simulation results further illustrate the dilemma conditions and provide representative examples to visualize the outcomes.

Abstracts:The UAV trajectory planning is the foundation of various applications. In real-world environments, there are not only static obstacles but also dynamic obstacles, so the UAV needs to have dynamic obstacle avoidance capability. The existing dynamic obstacle avoidance frameworks have low perception efficiency and insufficient security. To address these issues, we propose an efficient dynamic obstacle perception and avoidance framework for robust real-time UAV trajectory planning. In the obstacle perception module, we use displacement vector to represent the motion of obstacles and propose a displacement vector correction method to improve the accuracy of point cloud cluster motion estimation. Then, we establish Kalman filters for dynamic obstacles to predict their trajectories. A map including static obstacles and dynamic obstacle Kalman filters is provided for the trajectory planning. To improve trajectory safety, we propose the dynamic obstacle active perception yaw trajectory generation method, which pays sufficient attention to dynamic obstacles during flight. Finally, we compare our proposed framework with the SOTA methods in both simulation and the real-world environments. Our proposed method reduces the collision rate by 17.8$\%$–62.1$\%$ and exhibits high operational efficiency.

Abstracts:Aircraft landing gear load monitoring helps detect structural problems early and prevents potential accidents. Load prediction methods are the most important part of load monitoring, directly determining the accuracy of landing gear load assessment. However, current approaches exhibit limitations such as insufficient modeling of nonlinearities, reliance on simulation-generated data, and failure to consider spatial dependencies among landing gear sensors. In this article, we propose a multiscale convolutional transformer (MSCT) model to address these issues and enhance load prediction performance. Specifically, we conducted ground calibration test on the right landing gear of a real aircraft, employing fiber Bragg grating (FBG) sensors to collect strain data corresponding to heading (X), longitudinal (Y), and axial (Z) load axes. The MSCT model integrates multiscale convolutional layers, positional encoding (PE), and cross-scale attention mechanisms to effectively capture spatial correlations and local-global dependencies among sensors. Comparative experiment demonstrate that MSCT achieves superior prediction accuracy and generalization capability, with mean absolute percentage errors (MAPE) of 2.3821%, 2.8064%, 0.6286%, 1.7606%, and 2.7387% for X, -X, Y, Z, and -Z directions, respectively. We also conducted an ablation study to show the benefits of each component in MSCT.

Abstracts:Time–frequency analysis (TFA) is valuable in crucial application domains such as biomedical sciences, communication systems, and environmental monitoring. Existing TFA approaches, including those based on neural network algorithms and synchrosqueezing transform, exhibit limitations when dealing with intersecting strong and weak instantaneous frequency (IF). To address this issue, we introduce an end-to-end neural network specifically designed for weak signal detection, WeakSig-TFANet (WTFA-Net), guided by traditional weak signal processing schemes. The network employs a combination of time-domain and convolutional windows to attenuate the sidelobes of strong signals in the convolutional input, demonstrating exceptional recognition capabilities in scenarios involving weak signals such as faint respiratory and cardiac signals. To enhance resolution in cases of IF intersections, the proposed model structure further integrates complex attention modules and multicoarse estimation basis pixel-level attention mechanisms. These mechanisms counteract the decline in the ability to detect adjacent signals, a decline caused by the widening of the main lobe due to time-domain windowing of strong signals. Through experiments across multiple application scenarios, including both simulated and real-world signals, WTFA-Net is shown to outperform existing methods in weak signal detection performance in vital sign monitoring, underwater acoustic detection, channel state information of Wi-Fi, and inverse synthetic aperture radar image generation.

Abstracts:This article investigates the nonhoming guidance problem with impact angle constraints and obstacle avoidance under dynamic modeling errors and external disturbances. First, the obstacle is modeled based on reconnaissance aircraft information, and the allowable impact angle range is determined. Subsequently, an optimal trajectory with impact angle constraints and obstacle avoidance is shaped using the Bézier curve. A robust enhanced guidance law is then developed for precise tracking of the reference trajectory based on dynamic tube model-predictive control (DTMPC), where boundary layer sliding-mode control is used as an auxiliary controller to compensate for external disturbances. Compared with conventional DTMPC, the proposed method not only accounts for first-order differential equations with uncertainty characteristics in the computation of the robust control invariant tube but also explicitly considers system state constraints. This ensures safe trajectory tracking across a wide range of initial conditions while achieving high accuracy in satisfying impact angle constraints. Numerical simulations are performed to support our findings.

Abstracts:Modern meteorological satellites possess high-precision positioning and high-quality imaging in Earth observation and climate monitoring, whose requisite is accurate attitude stabilization. Multiple imbalance disturbances generated by large rotational payloads and the structural vibration of flexible panels greatly affect attitude stability and in-orbit operation. To fulfill the robust stabilization of the large meteorological satellites under these disturbances, this article formulates a new control strategy consisting of imbalance observation, attitude determination, and controller design. First, aiming for a typical Fengyun satellite, the coupling dynamic model of its platform, payloads with imbalance, and flexible panel is established. Next, the imbalance torques with any form are estimated by a sliding-mode disturbance observer. Triaxial attitude determination in this system is extended to a wideband and micromagnitude by magnetohydrodynamic angular rate sensing. Then, an integral sliding-mode controller is developed to mitigate imbalance and vibration based on global system stability. Finally, the proposed strategy is fully demonstrated in the typical operating states of the target satellite. This control system is highly robust to disturbances and parameter variation, and superior triaxial attitude determination and stabilization are highlighted, especially in the indices of accuracy and stability.