In this paper, we propose inference algorithms for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. First, we define the generalized innovation for the recently proposed optimal filter for simultaneous input and state estimation  and show that the sequence is a Gaussian white noise. Then, we utilize this whiteness property of the generalized innovation, which reflects the estimation quality to form the likelihood function of the system model. Consequently, we employ the multiple model (MM) approach based on the likelihood function for inferring the hidden mode of switched linear stochastic systems. Algorithms for both static and dynamic MM estimation are presented and compared using a simulation example of vehicles at an intersection with switching driver intentions.
|Title of host publication
|53rd IEEE Conference on Decision and Control,CDC 2014
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2014
|2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014 → Dec 17 2014
|Proceedings of the IEEE Conference on Decision and Control
|2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
|12/15/14 → 12/17/14
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization