TY - GEN
T1 - On the bias of the SIR filter in parameter estimation of the dynamics process of state space models
AU - Li, Tiancheng
AU - Rodríguez, Sara
AU - Bajo, Javier
AU - Corchado, Juan M.
AU - Sun, Shudong
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - As a popular nonlinear estimation tool, the sampling importance resampling (SIR) filter has been applied with the expectation-maximization (EM) principle, including the typical maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation, for estimating the parameters of the state space model (SSM). This paper concentrates on an inevitable bias existing in the EM-SIR filter for estimating the dynamics process of the SSM. It is analyzed that the root reason for the bias is the sample impoverishment caused by the resampling procedure employed in the filter. A process noise simulated for the particle propagation that is larger than the real noise involved with the true state will be helpful to counteract sample impoverishment, thereby providing better filtering result. Correspondingly, the EM-SIR filter tends to yield a biased (larger-than-the-truth) estimate of the process noise if it is unknown and needs to be estimated. The bias is elaborated via a straightforward roughening approach by means of both qualitative logical deduction and quantitative numerical simulation. However, it seems hard to fully remove this bias in practice.
AB - As a popular nonlinear estimation tool, the sampling importance resampling (SIR) filter has been applied with the expectation-maximization (EM) principle, including the typical maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation, for estimating the parameters of the state space model (SSM). This paper concentrates on an inevitable bias existing in the EM-SIR filter for estimating the dynamics process of the SSM. It is analyzed that the root reason for the bias is the sample impoverishment caused by the resampling procedure employed in the filter. A process noise simulated for the particle propagation that is larger than the real noise involved with the true state will be helpful to counteract sample impoverishment, thereby providing better filtering result. Correspondingly, the EM-SIR filter tends to yield a biased (larger-than-the-truth) estimate of the process noise if it is unknown and needs to be estimated. The bias is elaborated via a straightforward roughening approach by means of both qualitative logical deduction and quantitative numerical simulation. However, it seems hard to fully remove this bias in practice.
KW - Expectation-maximization
KW - Parameter estimation
KW - Particle filter
UR - http://www.scopus.com/inward/record.url?scp=84931312985&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19638-1_10
DO - 10.1007/978-3-319-19638-1_10
M3 - 会议稿件
AN - SCOPUS:84931312985
T3 - Advances in Intelligent Systems and Computing
SP - 87
EP - 95
BT - Distributed Computing and Artificial Intelligence, 12th International Conference, DCAI 2015
A2 - Omatu, Sigeru
A2 - Malluhi, Qutaibah M.
A2 - Bocewicz, Grzegorz
A2 - González, Sara Rodríguez
A2 - Bucciarelli, Edgardo
A2 - Giulioni, Gianfranco
A2 - Iqba, Farkhund
PB - Springer Verlag
T2 - 12th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2015
Y2 - 3 June 2015 through 5 June 2015
ER -