This paper proposes a time-based efficient global path generation method for multiple ground maneuvering platforms operating under a manned-unmanned teaming(MUM-T) framework. Using road-network information, the proposed method simultaneously generates (i) global routes connecting each platform’s start and target positions and (ii) corresponding time schedules for each route segment.By explicitly modeling the space-time occupancy of road segments during the search process, the proposed approach prevents simultaneous usage of the same road segments and produces collision-free trajectories. As a result, multiple platforms can reach their destinations while minimizing overall mission time and alleviating bottlenecks in constrained road networks.The effectiveness of the proposed algorithm is validated through simulation experiments conducted on a virtual two-dimensional map of a military training area located in Pocheon, Gangwon-do, Republic of Korea. The simulation results demonstrate that the proposed method successfully resolves conflicts caused by concurrent road usage and dynamically adapts to changes in mission conditions.The proposed technique is expected to serve as a core enabling technology for future ground MUM-T operations and next-generation battlefield combat systems.
This study investigates the influence of cooling channel fabrication methods on the thermal and structural stability of high-load reflectors. Two prototypes were compared: Reflector A, featuring cooling pipes welded to the rear surface, and Reflector B, incorporating internal channels created via a gun-drilling process. Thermal performance was evaluated by monitoring temperatures at nine points on both surfaces, and structural integrity was assessed through four-point displacement measurements. Experimental results revealed that Reflector A exhibited a significantly higher surface temperature(approximately 100 °C more than B) and a steeper thermal gradient. Consequently, Reflector A experienced 9 mm more thermal displacement due to increased thermal stress and heat transfer at the weld joints. In contrast, the integrated internal channels in Reflector B provided superior heat dissipation and minimized warping. These findings demonstrate that monolithic cooling structures are essential for maintaining the optical precision and structural reliability of reflectors under high temperature conditions.
Turbo shaft Engine accessory gearbox Housing should be designed to endure shock load while Engine operates. MIL-STD-810H provides basic guideline for shock load condition on aircraft structure. In this study, by Modal Superposition method, transient analysis and response spectrum analysis of shock load condition specified in MIL-STD-810H were performed on Gearbox Housing. Maximum stress occurred in shock load condition and the value yield strength devided by maximum stress was Factor of Safety. Finally, Safety margin was compared with 1.25, which is from NASA Criteria “Yield Design Factor”.
The operational environment in modern battlefields is rapidly evolving, with increasing missions in urban indoor spaces. Swarm micro-robots are widely used for reconnaissance to reduce risks to human personnel. To support effective operations, advanced swarm control technologies are required for navigation and cooperation in complex environments. This paper proposes a Multi-Agent Reinforcement Learning(MARL)-based positioning control algorithm that dynamically maintains Line-of-Sight(LoS) connectivity between agents. Unlike conventional methods assuming static or omnidirectional communication, relay robots reposition themselves in real time to optimize network topology and maintain stable information flow. The reward function incorporates LoS validation and distance-based signal attenuation, encouraging visibility-preserving formations. Simulation results demonstrate improved communication reliability and spatial coordination in uncertain environments.
This paper proposes an event-driven AODV routing optimization for highly dynamic Flying Ad hoc Networks (FANETs). Standard AODV relies on active route timeouts, leading to high packet loss and inefficient resource usage when network topologies rapidly change. To address this, we design an extended HELLO message to diminish RREQ flooding overhead and introduce a novel RDISC(Route Discovery) control message. The RDISC mechanism allows nodes to proactively trigger route updates upon link recovery events while maintaining ongoing sessions. Simulation results demonstrate that the proposed protocol significantly minimizes traffic loss and guarantees a stable topology update time independent of timeout configurations, effectively maximizing network efficiency in multi-hop drone swarm operations.
Experimental investigations were conducted to measure airblast parameters from TNT surface bursts at scaled distances of 0.94 to 6.79 m/kg1/3. Pressure-time histories of incident airblast waves were recorded using PCB sensors and processed to determine peak overpressure, impulse, and positive phase duration. These parameters were compared with Kingery-Bulmash(K-B) blast curves. Experimental peak overpressures were approximately 1.17 times higher than K-B predictions, while impulses showed excellent agreement(0.96 times K-B values). The elevated peak overpressures are likely due to improved precision in modern pressure sensors and digital data acquisition systems. These findings suggest that K-B blast parameters, limited by the technology of their time, require updating with data from contemporary instrumentation to enhance the accuracy of blast analysis and protective design applications.
This study presents an integrated machine learning framework for separating narrowband components and extracting Lloyd's mirror interference patterns from ship-radiated noise(SRN) spectrograms. The proposed methodology employs Independent Vector Analysis to separate narrowband spectral components from multi-hydrophone acoustic signals, subsequently applying DBSCAN and RANSAC algorithms for robust identification of parabolic Lloyd's mirror patterns in residual spectrograms. Experimental validation utilizing SRN data acquired during the SAVEX-15 sea trials demonstrates effective narrowband component separation, as verified through DEMON and LOFAR analyses, alongside accurate pattern extraction capabilities. The unsupervised framework exhibits enhanced reliability under adverse noise conditions and enables precise closest point of approach(CPA) estimation. The developed methodology offers an automated and robust solution for SRN analysis, significantly improving acoustic signal interpretation and target identification capabilities in maritime defense applications.
The Two-Sting Rig(TSR) is a wind tunnel test device designed to simulate the six degrees of freedom(6-DOF) motion of a store separating from its carrier platform. In such confined and high-risk environments, real-time collision detection is essential to prevent equipment damage and ensure operational safety. This paper proposes a real-time collision detection method that models the TSR and surrounding wind tunnel structures, such as aircraft models and support stings, using simplified geometric primitives. The method is specifically designed for enclosed test environments where external sensing is impractical due to structural constraints, and instead relies solely on internal motor feedback. The proposed algorithm is integrated into the TSR control software and validated across multiple operational scenarios. Experimental results confirm that the method reliably detects proximity risks in real-time, with execution times averaging under 0.95 ms, ensuring seamless integration into the 20 ms control loop. These findings demonstrate the algorithm’s effectiveness and practical suitability for real-time integration in wind tunnel operations.
Electronic warfare equipment operates in various environments such as fixed-wing, rotary-wing aircraft, ships, and ground systems, and controls different types of sensors and countermeasures based on the concept of equipment operation. As the concept of weapon system operation becomes more advanced, electronic warfare equipment must meet a wide range of requirements, and the software installed on it must be developed on time while ensuring reliability and quality. Software product line engineering is a methodology for efficiently developing and maintaining various software products used in specific domains by systematically managing commonality and variability, which helps reduce development costs and time while improving quality. In this paper, we apply software product line engineering to the electronic warfare software family and model the variability of electronic warfare software.
Traditional single task-multi robot-instantaneous assignment(ST-MR-IA) methods often suffer from rigidity under resource constraints and unbalanced resource usage. To address these issues, this paper proposes a genetic algorithm-based approach for coalition formation and task allocation in heterogeneous multi-robot systems(MRS) operating in unstructured environments. Leveraging GA flexibility, the proposed method identifies near-optimal solutions even with insufficient resources. By incorporating spatial cohesion, it groups proximal robots to minimize path deviation, ensuring consistent travel distances and balanced resource consumption. Gazebo simulations across three mission scenarios validate that this approach significantly improves mission feasibility and efficiency compared to linear programming-based algorithms.
This study proposes an optimal deployment model for long-range air-control radars using a Benders decomposition approach. The model integrates GIS-based terrain visibility analysis and incorporates probabilistic scenarios reflecting radar destruction and failure rates to represent realistic operational uncertainty. A two-stage stochastic optimization framework was developed to minimize total installation cost and detection deficiency across the Korean Air Defense Identification Zone(KADIZ). Computational experiments show that the deterministic model achieved better performance in detection coverage and cost minimization. However, the stochastic model demonstrated its practical value by reflecting uncertainty in radar availability and providing more robust deployment decisions under potential disruption scenarios. The proposed framework contributes to improving the resilience and adaptability of national air surveillance planning under uncertain environments.
Despite their continued operation in legacy aircraft, CRT(Cathode-Ray Tube)-based Azimuth Indicators exhibit significant limitations, prompting the need for digital modernization. The upgraded display unit maintains mechanical compatibility with existing 3-ATI(Aircraft Type Indicator) interfaces while achieving reduced size and weight. Functionally, it extends beyond basic RWR(Radar Warning Receiver) symbology to integrate MAWS(Missile Approach Warning System) and DIRCM(Directional Infrared Counter Measures) alerts as well as BIT(Built In Test) information, and it supports NVIS(Night Vision Imaging System) operation for night missions. The prototype was subjected to functional and environmental validation, confirming compliance with operational requirements. The results demonstrate a practical modernization approach that enhances maintainability, situational awareness, and survivability of legacy aircraft.
Military rotorcraft must adhere to internal noise standards to minimize hearing loss during operation and enable effective voice communication in noisy environments. This study predicted the internal noise levels and suggested noise reduction of a coaxial rotorcraft under high-speed maneuvering conditions to ensure compliance with these standards. To achieve this, Statistical Energy Analysis(SEA) models for helicopters were validated, and a new SEA model specific to the high-speed coaxial rotorcraft was suggested. Also, frequency spectrum of internal noise sources such as coaxial rotor, engines, and main gear box was scaled for the coaxial rotorcraft in high-speed maneuvering. As the sources were scaled for a high-speed coaxial rotorcraft, the internal noise levels at cockpit and cabin was predicted to exceed the design limit. This study proposed sound-proof panel modeling and position to satisfy the design limit.
This research focuses on optimizing the airfoil shape tailored for cardboard drones, a significant asset in the Russia-Ukraine war due to their cost-effectiveness and operational impact. Despite their advantages in storage and transport, these drones require manual assembly, with considerable time spent on connecting wing ribs and spars. To enhance both ease of assembly and aerodynamic performance, we formulated an airfoil shape optimization problem incorporating manufacturability constraints specific to cardboard drone construction. We utilized NeuralFoil, a physics-informed machine learning approach, to overcome the limitations of traditional tools. By integrating NeuralFoil with a gradient-based optimization method, the proposed approach resulted in a new airfoil that not only improves aerodynamic efficiency over existing shapes but also simplifies the assembly process.
The Network Enabled Weapon(NEW) possesses weapon data link communication capabilities, enabling precision strikes through various operational processes, such as re-targeting and in-flight target update(IFTU), which breaks away from the conventional fire-and-forget method. In Link-K, messages and protocols are designed to support directives and responses, and transmit NEW status and profile. NEW profile transmission, one of the NEW operational concepts, involves transmitting various data or files, including target imagery, during NEW operations. NEW profile transmission concept, not found in existing tactical data links such as Link-16, is newly introduced in Link-K. To achieve this, we designed messages and protocols for transmitting NEW profiles, as well as messages and protocols for transmitting real-time data in stream form to address its limitations. Furthermore, to intensively transmit this data during NEW operations in time division multiple access(TDMA)-based tactical data link network and enhance network resource efficiency, we introduce the concept of shared time slot and propose a network design plan that incorporates these features. Then, we present the results of measuring the transmission time when transmitting the NEW profile with three sample files. The experimental results are expected to serve as a criterion for judging the target imagery transmission capability in NEW in the future.
Modern Integrated Air Defense Systems(IADS) must perform real-time threat evaluation and engagement control in multi target and multi weapon environments. As the number of targets and defensive interceptor assets increases, the computational burden on the engagement control process rises significantly, leading to potential bottlenecks. To address this challenge, this paper applies GPU based parallel processing in the engagement control process of the IADS to accelerate computational processes such as coordinate transformation. Results show that GPU processing time increases gradually with the workload but remains much lower than that of the CPU. This demonstrates the effectiveness of GPU parallel processing. The proposed approach enhances the multi target and multi weapon simultaneous engagement capability of the IADS and provides a technical foundation for implementing real time engagement control in complex defense scenarios.
In this study, the fluid forces acting during the discharging process of a projectile were analyzed, along with the surrounding flow field. The process of discharging a projectile from a underwater platform was divided into several stages, and computational analysis was performed to calculate the drag generated during interactions with the underwater platform. For the numerical analysis, the commercial software Fluent 21.1, based on the finite volume method, was used. A steady-state simulation was conducted using a pressure-velocity coupling algorithm. The results showed that the drag gradually increased from its initial minimum value during the discharge. Initially, a force aiding the direction of motion appeared; however, the force was observed to be directionally biased.
Bearing-only target motion analysis(BO-TMA) is a technique for estimating the position and velocity of a target from the target bearing angles, and it plays a significant role in anti-submarine warfare. However, in underwater environments, propagation phenomena such as acoustic refraction and depth differences between the ownship and the target can introduce vertical components into the received signals. The received conical angle comprises the vertical component together with bearing angle. Therefore, received conical angle for conventional TMA algorithms which ignore the vertical components for modeling the received signals may produce large errors. We utilize an acoustic propagation model that provides the received vertical angle that comprises the conical angle. Then TMA algorithm based on a particle filter and an extended Kalman filter are applied for performance comparison.
As autonomous driving technologies continue to evolve, their real-world applications are expanding beyond structured urban environments. However, off-road autonomous driving remains a challenging problem due to the unstructured and unpredictable nature of such terrains. For an unmanned ground vehicle (UGV) to drive reliably in off-road environments, a path planning algorithm must not only generate smooth trajectories that avoid abrupt changes but also ensure drivability by adapting to irregular terrain features. In this paper, we propose a novel and efficient path planning algorithm tailored for off-road driving. Our method, called two-step MPC-PSO, combines Model Predictive Control (MPC) with Particle Swarm Optimization (PSO) to generate optimal paths within a limited computational budget. We also design a cost function that explicitly accounts for off-road conditions to enhance terrain adaptability. We validate our approach through experiments conducted at two off-road test sites. The results demonstrate that our method generates paths with low maximum curvature and sufficient path length, enabling smooth and continuous driving in unstructured environments.
Tactical platooning enables autonomous vehicles to operate in tightly coordinated formations, which is especially critical in military scenarios where they must execute evasive maneuvers and synchronized movements to avoid enemy threats in complex battlefield environments. Because this operation involves real-time data sharing with minimal latency to support the continuous exchange of large volumes of sensor data and control signals, stable and high-capacity links between vehicles are essential. To address this need, we propose a beam tracking technique to facilitate efficient inter-vehicle directional communication, particularly in highly dynamic and mobility-intensive situations. LiDAR (Light Detection and Ranging) is employed to accurately track the position data of the leading vehicle at a high frequency. To ensure robustness and reliability under adverse weather conditions where LiDAR measurements may be degraded or unavailable, RADAR (Radio Detection and Ranging) is incorporated into the UKF (Unscented Kalman Filter) as an alternative sensing modality. Additionally, a beam selection method is integrated to enhance the performance of the proposed technique. To verify its effectiveness, we developed a vehicle mobility and wireless channel model and random-sample simulation were conducted.
