Optimal state estimation in high noise

Optimal State Estimation in High Noise

The problem is examined of estimating the state of a linear dy namical system in the presence of high measurement noise. It is coneluded that optimal filter design maybe simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation )butonly onthesolutionof amatrix linear differential equation. For a related problem, that of estimating a signal s(t) g...

Approximating Optimal State Estimation

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for linear systems and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the exte...

Optimal State Estimation

In a post m o r t e m (after the fact) analysis, it is possible to wait for more observations to accumulate. In that case, the estimate can be improved by smoothing.-Andrew Jazwinski jJaz70, p. 1431 In previous chapters, we discussed how to obtain the optimal a priori and a posteriori state estimates. The a priori state estimate at time k, 2;, is the state estimate at time k based on all the me...

Multi-rate optimal state estimation

hidden state estimation in the state space model with first-order autoregressive process noise

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...