Electronic equipment mounted on military vehicles is exposed to external shocks and vibrations. This causes problems in tactical operation and also causes cost loss in equipment maintenance. Therefore, to optimize the test standards for military vehicles with vibration, dynamic characteristics should be analyzed and standardized through actual driving tests. In this paper, the internal structure of 2.5tons military vehicle is designed in the form of a drawer and lathe, and the dynamic characteristics are identified by experiment, and the design is proposed.

A fighter performing a reconnaissance mission is equipped with a pod that drives optical/infrared sensors for acquiring and identifying target information on the lower part of the fuselage. Due to the nature of the reconnaissance mission, the fighter performs high speed evasive maneuvers, and the resulting load should be considered importantly for the development of the pod. This paper concerns a numerical investigation into the inertial and aerodynamic loads of the airborne pod of high performance aircrafts. For the aerodynamic load analysis, the pylon and pod shapes are added to the fighter 3D model, and the commercial software was used for static and dynamic analysis. Considering the practical mission conditions, the common/extreme conditions were established respectively in the static and dynamic situations of pods and the driving torque could be tripled under dynamic conditions. In the analysis of inertia load, a 3-DOF model considering roll and turning maneuvers was derived by the Lagrangian method, and then the numerical integration method was applied to the analysis. As a results, it was conformed that the inertia load was generally induced at a low level compared to the aerodynamic load, but depending on the unbalance mass condition of the pod, the inertia load cannot be negligible.

As the application field of radar is expanded and the bandwidth increases, the number of radar sensors operating at the same frequency is continuously increasing. In this paper, we propose a method of analyzing interference when two pulse doppler radars are operated at the same frequency with different waveform which are designed independently. In addition, we show that even for a previously designed LFM waveforms, the interference can be suppressed without affecting the performance by changing the sign of the frequency slope by increasing/decreasing, or by modulating the pulses by the different codes. The interference suppression by different slopes is more effective for similar waveform and the suppression by the codes increases as the number of pulses increases. We expect this result can be extended to the cases where multiple radars are operated at the same frequency.

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

There has been necessity to supplement the fin database to improve the accuracy of low-fidelity aerodynamic solver for missile configuration. In this study, fin database is expanded by in-house solver, utilized in the triservice data the previously established into regions beyond means of CFD. Fin alone data of CFD analysis results in the original region is matched well with triservice data originated from the wind tunnel tests. Extensive fin aerodynamic data from CFD analysis is added to the existing database of the low-fidelity solver. For confirmation, aerodynamic characteristics of body-tail and body-canard-tail missile configurations is computed using upgraded low-fidelity solver at transonic region. The result using improved solver shows good agreements with wind tunnel test and CFD analysis results, which implies that it becomes more accurate.

Identifying warships contacted at sea is important to prepare for threats. It is necessary to obtain a basis to identify warships. In this study, we propose a 2-step model that acquires the warship's type and hullnumber with identification information from the warship images. The model classifies the warship's type and detects its hullnumber area by applying object detection, then recognizes hullnumber through text recognition algorithms. Proposed model achieved high performance by using state-of-the-art deep learning algorithms.

In this paper, we propose an opportunistic data relay scheme in narrowband multihop combat radio networks. Narrowband networks have physical restrictions on high-speed transmission. Furthermore, the topology changes dynamically due to the jamming of the enemy, signal interference between friendly forces, and movement of network entities. Therefore, the traditional relay scheme that collects topology information and calculates a relay path before transmission is unsuitable for such networks. Our proposed scheme does not collect topology information and transmits data opportunistically. The scheme can cause unnecessary data relaying that is not related to data delivery to the destination node. However, for small networks, the effect of increasing network throughput by not gathering topology information is much greater than the effect of reducing throughput by unnecessary data relays. We demonstrate the performance superiority of the proposed scheme through simulation in the worst case of network topology.

Maritime patrol aircraft is an efficient solution for detecting submarines at sea. The aircraft can only detect submarines by sonobuoy, but the number of buoy is limited. In this paper, we present the online sonobuoy deployment method for estimating the location of submarines. We use Gaussian process regression to estimate the submarine existence probability map, and Bayesian optimization to decide the next best position of sonobuoy. Further, we show the performance of the proposed method by simulation.

Since military weapon accidents or breakdowns are directly linked to enormous damage, it is important to analyze the causes of weapons system accidents. Recently, in the defense sector, there have been cases in which budget has been saved through analysis of the causes of frequent breakdowns and improvement activities that have occurred in the process of operating weapon systems since 2015. But due to the nature of the defense sector, it is not easy to collect data and studies on weapons system accidents have been insufficient so far. Therefore, this study aims to investigate the causes and types of military weapon accidents by collecting military weapon accident data for military weapon systems and analyzing trends by weapon system classification through the analysis process. It analyzes statistically and visually through social network analysis, NodeXL. It is expected that this study will help improve the stability of the weapon system by reducing the number of military weapon accidents and failures.

When setting the RAM objectives, various data such as expert opinions and the historical data of similar types of equipment are used. However, many times subjectivity is involved in the process of merging and utilizing data, and there are many cases where some information is omitted or an ambiguous method is used. Most of the previous work focused only on the process or method of calculating values using well-organized data rather than manipulating raw data. But if the data manipulation process is not objective, it is difficult to guarantee the accuracy of the results even if the calculation logic and method are accurate. This study proposes a systematic data merging process used to set the RAM objectives using the evidence theory. The proposed method can be used to avoid information loss and merge the data objectively. Moreover, contribute to improving the accuracy of setting the RAM objectives in the future.

Supply in military operations has a significant impact on overall combat capability and efficiency. Therefore, modernization of military logistics is underway to ensure rapid and accurate distribution. And, effective warehouse management is paramount. This paper proposes a new product allocation model that uses a genetic algorithm. The model considers order frequency and mass of products because the military equipment is usually heavier than available products. A computer simulation shows that products are assigned to optimal locations and reduce the consumed energy for forklifts by more than 25 % with similar travel time. Also, we show the superiority of genetic algorithm by comparing them with other algorithms.