불확정 표적 모델에 대한 순환 신경망 기반 칼만 필터 설계
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김동범 | - |
dc.contributor.author | 정대교 | - |
dc.contributor.author | 임재혁 | - |
dc.contributor.author | 민사원 | - |
dc.contributor.author | 문준 | - |
dc.date.accessioned | 2023-03-13T07:16:33Z | - |
dc.date.available | 2023-03-13T07:16:33Z | - |
dc.date.created | 2023-03-08 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.issn | 1598-9127 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182491 | - |
dc.description.abstract | For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한국군사과학기술학회 | - |
dc.title | 불확정 표적 모델에 대한 순환 신경망 기반 칼만 필터 설계 | - |
dc.title.alternative | Application of Recurrent Neural-Network based Kalman Filter for Uncertain Target Models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 문준 | - |
dc.identifier.doi | 10.9766/KIMST.2023.26.1.010 | - |
dc.identifier.bibliographicCitation | 한국군사과학기술학회지, v.26, no.1, pp.10 - 21 | - |
dc.relation.isPartOf | 한국군사과학기술학회지 | - |
dc.citation.title | 한국군사과학기술학회지 | - |
dc.citation.volume | 26 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 10 | - |
dc.citation.endPage | 21 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002929414 | - |
dc.description.journalClass | 2 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Recurrent Neural Network(순환 신경망) | - |
dc.subject.keywordAuthor | Kalman Filter(칼만 필터) | - |
dc.subject.keywordAuthor | Extended Kalman Filter(확장 칼만 필터) | - |
dc.subject.keywordAuthor | State-Estimation(상태 관측) | - |
dc.subject.keywordAuthor | Target Tracking(표적 추적) | - |
dc.identifier.url | https://jkimst.org/journal/view.php?doi=10.9766/KIMST.2023.26.1.010 | - |
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