(英) On the Uniqueness of Spectral Density Function in an SDP Problem for the Estimation of Innovations Model (日) イノベーションモデル推定のためのSDP問題におけるスペクトル密度関数の一意性について
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(英) The authors proposed an SDP (Semi-definite programming) formulation for the identification of the Kalman gain and the covariance matrix of the innovations process, aiming at a consistent estimation. However, the rank of a coefficient matrix of the variables does not have a column full rank, so that the uniqueness of the solution is not clear. In this paper, it shown that the Kalman gain and the covariance of the innovation can be uniquely estimated under a certain condition. (日) 筆者らは,カルマンゲインととイノベーション過程の共分散行列の一致推定を目指して,それらの同定のための半正定値計画問題を提案した.しかしながら,最適化変数の係数行列は列フルランクとならないため,推定値の一意性は不明であった.本論文では,ある条件が満たされれば,カルマンゲインとイノベーションの共分散は一意に推定できることを示す.
Kenji IkedaandHideyuki Tanaka : On the Uniqueness of Spectral Density Function in an SDP Problem for the Estimation of Innovations Model, Proceedings of SICE Annual Conference 2018, (巻), (号), 1483-1488, Nara, Sep. 2018.
欧文冊子 ●
Kenji IkedaandHideyuki Tanaka : On the Uniqueness of Spectral Density Function in an SDP Problem for the Estimation of Innovations Model, Proceedings of SICE Annual Conference 2018, (巻), (号), 1483-1488, Nara, Sep. 2018.
関連情報
Number of session users = 2, LA = 0.77, Max(EID) = 374179, Max(EOID) = 1002067.