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Maneuvering Target Tracking With 3D Variable Turn Model and Kinematic Constraint

3D 가변 선회 모델 및 기구학적 구속조건을 사용한 기동표적 추적

  • Kim, Lamsu (Korea Advanced Institute of Science and Technology) ;
  • Lee, Dongwoo (Korea Advanced Institute of Science and Technology) ;
  • Bang, Hyochoong (Korea Advanced Institute of Science and Technology)
  • Received : 2020.08.18
  • Accepted : 2020.10.06
  • Published : 2020.11.01

Abstract

In this paper, research on estimation of states of a target of interest using Line Of Sight(LOS) angle measurement is performed. Target's position, velocity, and acceleration are chosen to be the states of interests. The LOS measurement is known to be highly non-linear, making target dynamic modeling hard to be implemented into a filter. To solve this issue, the Pseudomeasurement equation was applied to the LOS measurement equation. With the help of this equation, 3D variable turn target dynamic model is applied to the filter model. For better performance, Kinematic Constraint is also implemented into the filter model. As for the filter, Bias Compensation Pseudomeasurement Filter (BCPMF) is used which is known for its robustness to initial conditions. Moreover, Two-Stage Kalman Filter (TSKF) form was also implemented to benefit from the parallel computation. As a result, TBCPMF 3DVT-KC is proposed and simulated to assess performance.

본 논문에서는 관측자가 얻을 수 있는 시선각(LOS) 측정값을 사용하여 관심표적의 상태변수를 추정하는 연구를 수행하였다. 관심상태변수는 표적의 위치, 속도 및 가속도로 설정하였다. 시선각 측정값은 필터에 표적운동모델 적용을 어렵게 하는 비선형성이 강한 측정값이다. 이러한 문제해결을 위해 가측정치 공식(Pseudomeasurement equation)을 사용하여 시선각 측정값 수식을 변경한 후 3D 가변선회(3D Variable Turn) 표적운동모델을 적용하였다. 또한 필터의 성능을 위해 기구학적구속조건(Kinematic Constraint)을 적용하였다. 필터는 초기조건에 강건한 특성을 가진 Bias Compensation Pseudomeasurement Filter (BCPMF)를 사용하였다. 병렬 계산의 이점을 위해 Two Stage Kalman Filter 형태를 추가적으로 적용하였다. 이 기법들을 사용하여 TBCPMF 3DVT-KC 필터를 제안하였고 시뮬레이션을 통해 성능을 확인하였다.

Keywords

References

  1. Barber, D. B., Redding, J. D., McLain, T. W., Beard, R. W. and Taylor, C. N., "Vision-Based Target Geo-location using a Fixed-wing Miniature Air Vehicle," Journal of Intelligent and Robotic Systems, Vol. 47, No. 4, 2006, pp. 361-382. https://doi.org/10.1007/s10846-006-9088-7
  2. Zhao, X., Pu, F., Wang, Z., Chen, H. and Xu, Z., "Detection, Tracking, and Geolocation of Moving Vehicle from UAV using Monocular Camera," IEEE Access, Vol. 7, 2019, pp. 101160-101170. https://doi.org/10.1109/ACCESS.2019.2929760
  3. Wang, I., Dobrokhodov, V., Kaminer, I. and Jones, K., "On Vision-Based Target Tracking and Range Estimation for Small UAVs," AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005, p. 6401.
  4. Zhang, L., Deng, F., Chen, J., Bi, Y., Phang, S. K., Chen, X. and Chen, B. M., "Vision-Based Target Three-Dimensional Geolocation using Unmanned Aerial Vehicles," IEEE Transactions on Industrial Electronics, Vol. 65, No. 10, 2018, pp. 8052-8061. https://doi.org/10.1109/tie.2018.2807401
  5. Dobrokhodov, V. N., Kaminer, I. I., Jones, K. D. and Ghabcheloo, R., "Vision-Based Tracking and Motion Estimation for Moving Targets using small UAVs," 2006 American Control Conference, 2006, pp. 1428-1433.
  6. Campbell, M. and Wheeler, M., "A Vision Based Geolocation Tracking System for UAV's," AIAA Guidance, Navigation, and Control Conference and Exhibit, 2006, p. 6246.
  7. Zhuo, J., Sun, L., Yang, Y. and Zhao, X., "A Target Localization Method for UAV Image Sequences Based on DEM Matching," 2016 9th International Symposium on Computational Intelligence and Design(ISCID), Vol. 2, 2016, pp. 215-218.
  8. Hamidi, M. and Samadzadegan, F., "Precise 3D Geo-location of UAV Images using Geo-Referenced Data," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40, 2015.
  9. Li, X. R. and Jilkov, V. P., "Survey of Maneuvering Target Tracking Part I Dynamic Models," IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 4, 2003, pp. 1333-1364. https://doi.org/10.1109/TAES.2003.1261132
  10. Maybeck, P. S., "Stochastic models, estimation, and control," Academic Press, 1982.
  11. Friedland, B., "Treatment of Bias in Recursive filtering," IEEE Transactions on Automatic Control, Vol. 14, No. 4, 1969, pp. 359-367. https://doi.org/10.1109/TAC.1969.1099223
  12. Kim, G. H., "Adaptive Filter Design for a Fault Tolerant Navigation System," Ph. D thesis, Seoul National University, 2006.
  13. Tahk, M. and Speyer, J., "Use of Intermittent Maneuvers for Miss Distance Reduction in Exoatmospheric Engagements," Guidance, Navigation and Control Conference, 1989, p. 3547.
  14. He, S., Wang, J. and Lin, D., "Three Dimensional Bias Compensation Pseudomeasurement Kalman Filter for Bearing-Only Measurement," Journal of Guidance, Control, and Dynamics, Vol. 41, No. 12, 2018, pp. 2678-2686. https://doi.org/10.2514/1.g003785
  15. Singer, R. A., "Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets," IEEE Transactions on Aerospace and Electronic Systems, Vol. 4, 1970, pp. 473-483. https://doi.org/10.1109/TAES.1970.310128
  16. Grewal, M. S. and Andrews, A. P., "Applications of Kalman Filtering in Aerospace 1960 to the Present (Historical Perspectives)," IEEE Control Systems Magazine, Vol. 30, No. 3, 2010, pp. 69-78. https://doi.org/10.1109/MCS.2010.936465