• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.55-64
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• 2013

In this paper, the HILS (Hardware In-the-Loop Simulation) system to analyze and to verify the performance of the targeting pod is addressed. The main functions of the targeting pod is acquiring and tracking targets to guide a LGB (Laser Guided Bomb) to the targets. For the analysis of targeting pod, the real time simulate images generation of IR and daylight cameras, sever control technology, and the analysis of laser transfer characteristics are necessary. For the real time image generation and the laser transfer characteristics analysis, off-the-shelf SDK(Software Development Kit) OKTAL-SE is used. For the servo controller, well-proven mechanism in the previous program is applied to increase servo control accuracy. To analyze the performance of a targeting pod in a realistic environment, 1553B, ARINK818 interface and etc. which are actually implemented in real combat aircrafts are applied in the system. By using the developed HILS system, the performance of currently operating targeting pods in real combat aircrafts can be analyzed and predicted. Additionally, the relationship between overall system performance and each module performance can be analyzed, the currently developed HILS system is expected to be a very useful tool to generate system development requirements of targeting pods and to reduce any possible future development risks.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.