Design of Filter Using Covariance Information in Continuous-Time Stochastic Systems with Nonlinear Observation Mechanism

スポンサーリンク

概要

• 論文の詳細を見る

• This paper proposes a new design method of a nonlinear filtering algorithm in continuous-time stochastic systems. The observed value consists of nonlinearly modulated signal and additive white Gaussian observation noise. The filtering algorithm is designed based on the same idea as the extended Kalman filter is obtained from the recursive least-squared Kalman filter in linear continuous-time stochastic systems. The proposed filter necessitated the information of the autocovariance function of the signal, the variance of the observation noise, the nonlinear observation function and its differentiated one with respect to the signal. The proposed filter is compared in estimation accuracy with the MAP filter both theoretically and numerically.

• 一般社団法人電子情報通信学会の論文
• 1998-05-25

著者

• NAKAMORI Seiichi

  the Faculty of Education, Kagoshima University

• Nakamori Seiichi

  The Faculty Of Education Kagoshima University

関連論文

• Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay (Digital Signal Processing)
• Design of Filter Using Covariance Information in Continuous-Time Stochastic Systems with Nonlinear Observation Mechanism
• Design of Linear Continuous-Time Stochastic Estimators Using Covariance Information in Krein Spaces

スポンサーリンク