• Title/Summary/Keyword: 상대 자세 추정

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A Study on Trainer and Cover Recognition Algorithm for Posture Recognition of Virtual Shooting Trainer (가상 사격 훈련자 자세인식을 위한 훈련자와 엄폐물 인식 알고리즘 연구)

  • Kim, Hyung-O;Hong, ChangHo;Cho, Sung Ho;Park, Youster
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.298-300
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    • 2021
  • The Ministry of National Defense decided to build a realistic combat simulation training system based on virtual reality and augmented reality in accordance with the expansion of the scientific training system of "Defense Reform 2.0". The realistic combat simulation training system should be able to maximize the tension and training effect as in actual combat through engagement between trainers. In addition, it should be possible to increase the effectiveness of survival training at the same time as shooting training similar to actual combat through cover training. Previous studies are suitable techniques to improve the shooting precision of the trainee, but it is difficult to practice bilateral engagement like in actual combat, and it is particularly insufficient for combat shooting training using cover. Therefore, in this paper, we propose a S/W algorithm for generating a virtual avatar by recognizing the shooting posture of the opponent on the screen of the virtual shooting trainer. This S/W algorithm can recognize the trainer and the cover based on the depth information acquired through the depth sensor and estimate the trainer's posture.

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Descent Dataset Generation and Landmark Extraction for Terrain Relative Navigation on Mars (화성 지형상대항법을 위한 하강 데이터셋 생성과 랜드마크 추출 방법)

  • Kim, Jae-In
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1015-1023
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    • 2022
  • The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

A Study on Shell-Shaped Target Classification Using RCS and Fuzzy Classifier (RCS와 퍼지 구분기를 이용한 포탄 형태의 표적 식별기법에 대한 연구)

  • Lee, Seung-Jae;Jung, Sung-Jae;Kang, Byung-Soo;Na, Hyung-Gi;Kim, Hyun;Kim, Kyung-Tae
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.25 no.5
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    • pp.576-584
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    • 2014
  • In this paper, a study on the optimization of fuzzy classifier using radar cross section(RCS) values is presented to classify shell-shaped targets. Method of moments(MOM) is exploited to construct RCS database of generic shell-shaped targets in uniform angular intervals. Relative orientations are estimated from various flight scenarios of shell-shaped targets, and associated RCS values are interpolated from the generated RCS database with uniform angular intervals. Initial membership functions are determined using the interpolated RCS values, and particle swarm optimization(PSO) is utilized to optimize the membership functions of the fuzzy classifier in terms of probability of correct classification.

Guidance Filter Design Based on Strapdown Seeker and MEMS Sensors (스트랩다운 탐색기 및 MEMS 센서를 이용한 유도필터 설계)

  • Yun, Joong-Sup;Ryoo, Chang-Kyung;Song, Taek-Lyul
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.37 no.10
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    • pp.1002-1009
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    • 2009
  • Precision guidance filter design for a tactical missile with a strapdown seeker aided by low-cost strapdown sensors has been addressed in this paper. The low-cost strapdown sensors consist of an IMU with 3-axis accelerometers and gyroscopes, 3-axis magnetometers, and a barometer. Missile's position, velocity, attitude, and bias error of the barometer are considered as state variables. Since the state and measurement equations are highly nonlinear, we adopt UKF(Unscented Kalman Filter). The proposed guidance filter has a function of a navigation filter if target position error is not considered. In the case that the target position error is introduced, the proposed filter can effectively estimate the relative states of the missile to the true target. For specific engagement scenarios, we can observe that observability problems occur.

Physical Offset of UAVs Calibration Method for Multi-sensor Fusion (다중 센서 융합을 위한 무인항공기 물리 오프셋 검보정 방법)

  • Kim, Cheolwook;Lim, Pyeong-chae;Chi, Junhwa;Kim, Taejung;Rhee, Sooahm
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1125-1139
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    • 2022
  • In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

Extraction of Ocean Surface Current Velocity Using Envisat ASAR Raw Data (Envisat ASAR 원시자료를 이용한 표층 해류 속도 추출)

  • Kang, Ki-Mook;Kim, Duk-Jin
    • Korean Journal of Remote Sensing
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    • v.29 no.1
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    • pp.11-20
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    • 2013
  • Space-borne Synthetic Aperture Radar(SAR) has been one of the most effective tools for monitoring quantitative oceanographic physical parameters. The Doppler information recorded in single-channel SAR raw data can be useful in estimating moving velocity of water mass in ocean. The Doppler shift is caused by the relative motion between SAR sensor and the water mass of ocean surface. Thus, the moving velocity can be extracted by measuring the Doppler anomaly between extracted Doppler centroid and predicted Doppler centroid. The predicted Doppler centroid, defined as the Doppler centroid assuming that the target is not moving, is calculated based on the geometric parameters of a satellite, such as the satellite's orbit, look angle, and attitude with regard to the rotating Earth. While the estimated Doppler shift, corresponding to the actual Doppler centroid in the situation of real SAR data acquisition, can be extracted directly from raw SAR signal data, which usually calculated by applying the Average Cross Correlation Coefficient(ACCC). The moving velocity was further refined to obtain ocean surface current by subtracting the phase velocity of Bragg-resonant capillary waves. These methods were applied to Envisat ASAR raw data acquired in the East Sea, and the extracted ocean surface currents were compared with the current measured by HF-radar.