A new prediction algorithm using covariance information in linear continuous systems
Automatica (Journal of IFAC)
Application of kalman filter in time series software reliability growth model
MATH'05 Proceedings of the 8th WSEAS International Conference on Applied Mathematics
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.98 |
The recursive least-squares filter and fixed-point smoother for the multivariate signal are designed in linear discrete-time systems. The estimators require the information of the system matrix, the observation matrix, and the variances of the state and white Gaussian observation noise in the signal generating model besides the updated observed value. By appropriate choices of the observation matrix and the state variables, the state-space model corresponding to the AR (autoregressive) model of order n is introduced. Here, some elements of the system matrix consist of the AR parameters. This paper proposes an iterative technique regarding the estimation of the variance of white Gaussian observation noise by use of the autocovariance data of the observed value. It is shown that the system matrix, the multivariate AR parameters, the variances of the state and observation noise, and the crossvariance of the input noise with the observation noise are estimated from the sampled autocovariance data of finite number. As a consequence, the multivariate signal is estimated in terms of the observed value and its autocovariance data.