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Integrated Algorithm for Identification of Long Range Artillery Type and Impact Point Prediction With IMM Filter (IMM 필터를 이용한 장사정포의 탄종 분리 및 탄착점 예측 통합 알고리즘)

  • Jung, Cheol-Goo;Lee, Chang-Hun;Tahk, Min-Jea;Yoo, Dong-Gil;Sohn, Sung-Hwan
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.50 no.8
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    • pp.531-540
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    • 2022
  • In this paper, we present an algorithm that identifies artillery type and rapidly predicts the impact point based on the IMM filter. The ballistic trajectory equation is used as a system model, and three models with different ballistic coefficient values are used. Acceleration was divided into three components of gravity, air resistance, and lift. And lift acceleration was added as a new state variable. The kinematic condition that the velocity vector and lift acceleration are perpendicular was used as a pseudo-measurement value. The impact point was predicted based on the state variable estimated through the IMM filter and the ballistic coefficient of the model with the highest mode probability. Instead of the commonly used Runge-Kutta numerical integration for impact point prediction, a semi-analytic method was used to predict impact point with a small amount of calculation. Finally, a state variable initialization method using the least-square method was proposed. An integrated algorithm including artillery type identification, impact point prediction and initialization was presented, and the validity of the proposed method was verified through simulation.

Implementation of Acceleration Sensor-based Human activity and Fall Classification Algorithm (가속도 센서기반의 인체활동 및 낙상 분류를 위한 알고리즘 구현)

  • Hyun Park;Jun-Mo Park;Yeon-Chul, Ha
    • Journal of the Institute of Convergence Signal Processing
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    • v.23 no.2
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    • pp.76-83
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    • 2022
  • With the recent development of IT technology, research and interest in various biosignal measuring devices is increasing. As an aging society is in full swing, research on the elderly population using IT-related technologies is continuously developing. This study is about the development of life pattern detection and fall detection algorithm, which is one of the medical service areas for the elderly, who are rapidly developing as they enter a super-aged society. This study consisted of a system using a 3-axis accelerometer and an electrocardiogram sensor, collected data, and then analyzed the data. It was confirmed that behavioral patterns could be classified from the actual research results. In order to evaluate the usefulness of the human activity monitoring system implemented in this study, experiments were performed under various conditions, such as changes in posture and walking speed, and signal magnitude range and signal vector magnitude parameters reflecting the acceleration of gravity of the human body and the degree of human activity. was extracted. And the possibility of discrimination according to the condition of the subject was examined by these parameter values.