• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.57-65
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• 2015

Global Navigation Satellite System (GNSS) including Global Positioning System (GPS) is an important element for navigation of both the military and civil Unmanned Aerial Vehicle (UAV). Contrary to the military UAVs, the civil UAVs use the civil signals which are unencrypted, unauthenticated and predictable. Therefore if the civil signals are counterfeited, the civil UAV’s position can be manipulated and the appropriate movement of the civil UAV to the target point is not achieved. In this paper, spoofing on the autonomous navigation UAV is implemented through field experiments. Although the demanded conditions for appropriate spoofing attack exists, satisfying the conditions is restricted in real environments. So, the Way-point of the UAV is assumed to be known for experiments and assessments. Under the circumstances, GPS spoofing signal is generated based on the Software-based GNSS signal generator. The signal is emitted to the target UAV using the antenna of the spoofer and the effect of the signal is analyzed and evaluated. In conclusion, taking the UAV to the target point is hardly feasible. To implement the spoofing as expectation, the position and guidance system of the UAV has to be known. Additionally, the GPS receiver on the UAV could be checked whether it appropriately tracks the spoofing signal or not. However, the effect of the spoofing signal on the autonomous UAV has been verified and assessed through the experimental results. Spoofing signal affects the navigation system of the UAV so that the UAV goes off course or shows an abnormal operation.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.19-26
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• 2004

This paper deals with the automatic flight control system for an unmanned target drone which is operated by an army as an anti-air gun shooting training. By automation of unmanned target drone that is manually operated by external pilot, pilot can reduce workload and an army can reduce the budget. Most UAVs which are developed until today use high-cost sensors as AHRS and IMU to measure the attitude, but those are contradictory for the reduction of budget. This paper says the development of low-cost automatic flight control system which makes possible of automatic flight with low-cost sensors. We have developed the integrated automatic flight control system by integrating electricity module, switching module, monitoring module and RC receiver as an one module. We also prove the performance of automatic flight control system by flight test.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.63-72
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• 2010

In the future warfare, the importance of the counter-fire operation is increasing. The counter-fire operation is divided into offensive counter-fire operation and defensive counter-fire operation. Reviewing the researches done so far, the detection asset of offensive counter-fire operation called UAV(Unmanned Aerial Vehicle) and its operational effectiveness analysis is continually progressing. However, the analysis of the detection asset of defensive counterfire called Target Acquisition Radar(TAR) and its quantitative operational effectiveness are not studied yet. Therefore, in this paper, we studied operational effectiveness of TAR using C2 Theory & MANA Simulation model, and showed clear quantitative analysis results by comparing both cases of using TAR and not using TAR.

• The Journal of Korea Robotics Society
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• v.12 no.4
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• pp.432-440
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• 2017

In this paper, we propose a jellyfish distribution recognition and monitoring system using a UAV (unmanned aerial vehicle). The UAV was designed to satisfy the requirements for flight in ocean environment. The target jellyfish, Aurelia aurita, is recognized through convolutional neural network and its distribution is calculated. The modified deep neural network architecture has been developed to have reliable recognition accuracy and fast operation speed. Recognition speed is about 400 times faster than GoogLeNet by using a lightweight network architecture. We also introduce the method for selecting candidates to be used as inputs to the proposed network. The recognition accuracy of the jellyfish is improved by removing the probability value of the meaningless class among the probability vectors of the evaluated input image and re-evaluating it by normalization. The jellyfish distribution is calculated based on the unit jellyfish image recognized. The distribution level is defined by using the novelty concept of the distribution map buffer.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.251-252
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• 2012

본 논문에서는 UAV(Unmanned Aerial Vehicle)을 이용하여 목표물을 찾고 목표물의 위치가 멀거나 목표물이 이동 시에도 기지국까지 지속적인 통신 연결을 제공하기 위해 UAV 집단을 제어하는 방안을 제안한다. 기존의 통신 시설을 이용할 수 없으며, UAV들 간에만 무선 랜 통신이 가능한 전시상황이나 특수 재난 상황에서 사용되는 것을 가정하였다. 제안 방안은 UAV들이 탐색지역 내에 목표물을 찾은 후에 목표물에 대한 정보를 기지국까지 전달하기 위하여 UAV들을 이동시킨다. 목표물이 먼 곳에 위치할 시에는 UAV들이 기지국까지의 통신 연결을 주기적이라도 유지하기 위해 UAV가 다른 UAV의 통신 범위까지 이동하여 정보를 전달하고 원래 위치로 복귀하는 방안과, 목표물이 이동할 때 목표물을 추적하며 기지국과의 연결성을 유지하는 방안을 제안한다. 이와 같은 과정들은 NS-2를 사용한 모의실험을 통하여 제안되는 기법을 검증하고 성능을 평가한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1115-1123
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• 2011

This paper proposes an image processing algorithms for detection and tracking of aerial vehicles in short-range. Proposed algorithm detects moving objects by using image homography calculated from consecutive video frames and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi-Hypothesis Tracking method(PMHT). This algorithm can perform better than simple color-based detection methods since it can detect moving objects under complex background such as the ground seen during low altitude flight and consider the characteristics of vehicle dynamics. Furthermore, it is effective for the flight test due to the reduction of thresholding sensitivity against external factors. The performance of proposed algorithm is verified by applying to the onboard video obtained by flight test.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.3-10
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• 2014

Digital maps are made by experts who digitize the data from aerial image and field survey. And the digital maps are updated every 2 years in National Geographic Information Institute. Conventional Digitizing methods take a lot of time and cost. And geographic information needs to be modified and updated appropriately as geographical features are changing rapidly. Therefore in this paper, we modify the digital map updates the road information for rapid high-resolution aerial orthophoto taken at different times were performed HSI color conversion. Road area of the cassification was performed the region growing methods. In addition, changes in the target area for analysis by applying the CVA technique to compare the changed road area by analyzing the accuracy of the proposed extraction.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1245-1258
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• 2023

To efficiently manage forest resources, it is essential to deploy multiple unmanned aerial vehicles equipped with various sensors simultaneously. Consequently, the ground control station antenna should not only maintain continuous tracking of the target station but also minimize the impact of radio interference on other unmanned aerial vehicle stations. In this paper, we presented beam forming techniques based on the VPR algorithm within a ground control station constructed using a phased array antenna system. Through simulation experiments in diverse unmanned aerial vehicle operating environments, it was demonstrated that the presented method enables not only the continuous tracking of operational unmanned aerial vehicles but also the suppression of radio interference by establishing a continuous pattern null for multiple operational radio interference sources.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.114-123
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• 2016

The effective feature extraction method for unmanned aerial vehicle (UAV) detection is proposed and verified in this paper. The UAV engine sound is harmonic complex tone whose frequency ratio is integer and its variation is continuous in time. Using these characteristic, we propose the feature vector composed of a mean and standard deviation of difference value between fundamental frequency with 1st overtone as well as mean variation of their frequency. It was revealed by simulation that the suggested feature vector has excellent discrimination in target signal identification from various interfering signals including frequency variation with time. By comparing Fisher scores, three features based on frequency show outstanding discrimination of measured UAV signals with low signal to noise ratio (SNR). Detection performance with simulated interference signal is compared by MFCC by using ELM classifier and the suggested feature vector shows 37.6% of performance improvement As the SNR increases with time, the proposed feature can detect the target signal ahead of MFCC that needs 4.5 dB higher signal power to detect the target.