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

• NAKAMORI Seiichi

  Faculty of Education, Kagoshima University

抄録

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.

収録刊行物

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 81 (5), 904-912, 1998-05-25

  一般社団法人電子情報通信学会

キーワード

• continuous-time stochastic systems
• filtering
• rejection of observation noise
• nonlinear observation mechanism

詳細情報

• CRID: 1570009752449984384
• NII論文ID: 110003208001
• NII書誌ID: AA10826239
• ISSN: 09168508
• 本文言語コード: en
• データソース種別:
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