지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 973 - 980 (8page)
- DOI
- 10.5302/J.ICROS.2022.22.0146
이용수
초록· 키워드
오류제보하기
Several fields employ a method for estimating the source position of a multichannel time series signal received via multiple sensors. This requires an algorithm to estimate the position from the propagation time difference between the signals. Moreover, the algorithm needs to be modified according to the geometric condition of the propagation space, propagation speed in the medium, and noise caused by the reflector. However, since it is difficult to supplement the algorithm to respond individually to various propagation conditions, the more complex the propagation conditions, the less precise the source localization. To overcome these limitations, this study proposes a reinforcement learning algorithm to estimate the source location using simulations that generate multi-channel timeseries signals from random locations, which are propagated through three-dimensional space and received by multiple sensors. The reinforcement learning model estimates the source position by inputting the difference between the target signals and the signals generated from the random position as well as moving the positions to a new expected source positions where the differences can be minimized. By repeating the movement of the expected source positions, if the changes are repeated within the allowable minimum value in which the position retains in a specific region, the corresponding position can be estimated to be the source position. The proposed reinforcement learning model uses 300 samples from seven sensor positions at a sampling rate of 0.5 kHz and a propagation speed of 5000 m/s in a space with dimension of 1000 mm width, 1000 mm length, and 1000 mm height. From estimation of 1000 random source positions with the condition, the error was less than 50 mm in 90% or more. Thus, by reconfiguring the simulation environment to conform to a new target situation and learning the model, source localization models can be developed.
목차
Abstract
I. 서론
II. 가상환경 및 강화학습 문제 정의
III. 강화학습 환경구성
IV. 강화학습 알고리즘
V. 결과 및 고찰
VI. 결론
REFERENCES
참고문헌 (12)
참고문헌 신청
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Z. Huang, J. Xu, Z. Gong, H. Wang, and Y. Yan, “Source localization using deep neural networks in a shallow water environment,” The journal of the acoustical society of America, vol. 143, no. 5, pp. 2922-2932, 2018.
A. Ebrahimkhanlou and S. Salamone, “Single-sensor acoustic emission source localization in plate-like structures using deep learning,” Aerospace, vol. 5, no. 2, p. 50, 2018.
S.-J. Hyeon, T. Y. Kang, and C.-K. Ryoo, “A path planning for unmanned aerial vehicles using SAC (Soft Actor Critic) algorithm,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 2, pp. 138-145, 2022.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
Z. Huang, J. Xu, Z. Gong, H. Wang, and Y. Yan, “Source localization using deep neural networks in a shallow water environment,” The journal of the acoustical society of America, vol. 143, no. 5, pp. 2922-2932, 2018.
A. Ebrahimkhanlou and S. Salamone, “Single-sensor acoustic emission source localization in plate-like structures using deep learning,” Aerospace, vol. 5, no. 2, p. 50, 2018.
S.-J. Hyeon, T. Y. Kang, and C.-K. Ryoo, “A path planning for unmanned aerial vehicles using SAC (Soft Actor Critic) algorithm,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 2, pp. 138-145, 2022.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
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