TY - GEN
T1 - Generalized innovation and inference algorithms for hidden mode switched linear stochastic systems with unknown inputs
AU - Yong, Sze Zheng
AU - Zhu, Minghui
AU - Frazzoli, Emilio
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - 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 [1] 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.
AB - 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 [1] 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.
UR - http://www.scopus.com/inward/record.url?scp=84988223675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988223675&partnerID=8YFLogxK
U2 - 10.1109/CDC.2014.7039914
DO - 10.1109/CDC.2014.7039914
M3 - Conference contribution
AN - SCOPUS:84988223675
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3388
EP - 3394
BT - 53rd IEEE Conference on Decision and Control,CDC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
Y2 - 15 December 2014 through 17 December 2014
ER -