Title :
The interacting multiple model algorithm for systems with Markovian switching coefficients
Author :
Blom, Henk A P ; Bar-Shalom, Yaakov
Author_Institution :
Nat. Aerosp. Lab., Amsterdam, Netherlands
fDate :
8/1/1988 12:00:00 AM
Abstract :
An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients
Keywords :
Markov processes; filtering and prediction theory; linear systems; Markovian switching coefficients; approximate Bayesian filtering; dynamic multiple model systems; filtering; hypotheses merging; interacting multiple model algorithm; linear systems; Adaptive control; Adaptive filters; Automatic control; Filtering algorithms; Linear systems; Merging; Nonlinear filters; Programmable control; Silicon compounds; State feedback;
Journal_Title :
Automatic Control, IEEE Transactions on