| 摘要 |
State estimation of a system having multidimensional parameters, which are unknown, arbitrarily time-varying, but bounded, in addition to state variables, is performed by initializing the state estimate and matrices representing its covariance and bias coefficients which linearly relate initial state estimation errors to the parameter errors. System matrices Phi, Gamma, F, G and the mean value {overscore (lambda of unknown, time-varying, but bounded parameters lambda are determined. A matrix Lambda is generated, representing their physical bounds. The state estimate x(k|k) and matrices M(k|k) and D(k|k), characterizing the effects of measurement errors and parameter uncertainty, are extrapolated to generate x(k+1|k), M(k+1|k), and D(k+1|k). The measurement noise covariance N is determined. The filter gain matrix K is calculated. The state estimate is updated with the filter gain matrix K weighting the measurement z(k+1) and the extrapolated state estimate x(k+1|k) to generate the current system estimate x(k+1|k+1), by minimizing its total mean square error due to measurement errors and parameter uncertainty. The matrices M(k+1|k) and D(k+1|k) are updated with the filter gain matrix K to generate M(k+1|k+1) and D(k+1|k+1).
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