Tracking and identification of regime-switching systems using binary sensors

Authors:
G. Yin;Le Yi Wang;Shaobai Kan
Affiliations:
Department of Mathematics, Wayne State University, Detroit, MI 48202, United States;Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, United States;Department of Mathematics and Computer Science, John Jay College of Criminal Justice, CUNY, 445 West 59 Street, New York, NY 10019, United States
Venue:
Automatica (Journal of IFAC)
Year:
2009

Citing 10
Cited 0

System identification: theory for the user

System identification: theory for the user
Adaptive algorithms and stochastic approximations

Adaptive algorithms and stochastic approximations
Elements of information theory

Elements of information theory
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview

Automatica (Journal of IFAC)
Adaptive signal processing algorithms: stability and performance

Adaptive signal processing algorithms: stability and performance
On the Adaptive Control for Jump Parameter Systems viaNonlinear Filtering

SIAM Journal on Control and Optimization
Asymptotically efficient parameter estimation using quantized output observations

Automatica (Journal of IFAC)
Joint identification of plant rational models and noise distribution functions using binary-valued observations

Automatica (Journal of IFAC)
Hidden Markov processes

IEEE Transactions on Information Theory
LMS algorithms for tracking slow Markov chains with applications to hidden Markov estimation and adaptive multiuser detection

IEEE Transactions on Information Theory

Quantified Score

Hi-index	22.14

Visualization

Abstract

This work is concerned with tracking and system identification for time-varying parameters. The parameters are Markov chains and the observations are binary valued with noise corruption. To overcome the difficulties due to the limited measurement information, Wonham-type filters are developed first. Then, based on the filters, two popular estimators, namely, mean squares estimator (MSQ) and maximum posterior (MAP) estimator are constructed. For the mean squares estimator, we derive asymptotic normality in the sense of weak convergence and in the sense of strong approximation. The asymptotic normality is then used to derive error bounds. When the Markov chain is infrequently switching, we derive error bounds for MAP estimators. When the Markovian parameters are fast varying, we show that the averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and that can be estimated using empirical measures. Upper and lower error bounds on estimation errors are also established.