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RECURSIVE ESTIMATION OF IMPULSE RESPONSE FUNCTION USING COVARIANCE INFORMATION IN LINEAR CONTINUOUS STOCHASTIC SYSTEMS NAKAMORI, Seiichi 中森, 誠一 open access This paper proposes a new recursive least-squares (RLS) estimation algorithm for an impulse response function in linear continuous-time wide-sense stationary stochastic systems. It is assumed that the input signal to the unknown impulse response function is contaminated by additive white Gaussian observation noise. The output signal from the system related with the impulse response function is observed with additive white Gaussian noise. The impulse response function is estimated recursively in terms of the variance of the white Gaussian observation noise included in the input signal, the autocovariance function of the process before the observation noise is added to the input signal, and the crosscovariance function between the output observed value and the input observed value, concerning the system based on the unknown impulse response function. 鹿児島大学 Kagoshima University 2001-03-27 eng departmental bulletin paper VoR http://hdl.handle.net/10232/15340 https://ir.kagoshima-u.ac.jp/records/2475 03896692 AN00408518 鹿児島大学教育学部研究紀要. 自然科学編 Bulletin of the Faculty of Education, Kagoshima University. Natural science 52 55 65 https://ir.kagoshima-u.ac.jp/record/2475/files/AN00408518_v52_P55-65.pdf application/pdf 10.0 MB 2016-10-27 2001-03-27