<p>The problem of estimating the states of a dynamical system on the basis of output measurements is considered in detail. Some of the existing nonlinear estimation techniques are critically surveyed, these include the extended Kalman filter, the second-order filter, the innovations approach, and the invariant imbedding nonlinear filter. A new algorithm for nonlinear estimation is proposed which combines the invariant imbedding approach and the stochastic approximation algorithm for adaptively estimating the filter gain. The new algorithm is an iterative scheme which does not require knowledge of a priori input and measurement noise statistics. The proposed algorithm and the other techniques are used for the recursive state estimation of a satellite orbital trajectory. The results of simulation indicate the efficiency and reliability of the new algorithm. Convergence to the true state is achieved with much less computation when compared to the other methods.</p> / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/11755 |
| Date | 05 1900 |
| Creators | Abdel-Azim, Sanaa |
| Contributors | Sinha, N.K., Electrical Engineering |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0018 seconds