DocumentCode :
1765214
Title :
Active Classification for POMDPs: A Kalman-Like State Estimator
Author :
Zois, Daphney-Stavroula ; Levorato, Marco ; Mitra, U.
Author_Institution :
Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume :
62
Issue :
23
fYear :
2014
fDate :
Dec.1, 2014
Firstpage :
6209
Lastpage :
6224
Abstract :
The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations´ quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications, including sensor management for object classification and tracking, estimation of sparse signals, and radar scheduling.
Keywords :
Kalman filters; Markov processes; dynamic programming; least mean squares methods; state estimation; telecommunication control; wireless sensor networks; Gaussian measurement vectors; Kalman-like state estimator; MMSE filter; POMDP active classification; active observation control; control strategy optimization; finite-state Markov chain; minimum mean-squared error; partially observable Markov decision process; physical activity detection; radar scheduling; sensor management; state tracking problem; stochastic dynamic programming algorithm; wireless body sensing networks; Educational institutions; Error probability; Estimation; Markov processes; Radar tracking; Sensors; Technological innovation; Active classification; Kalman-like filter; Kalman-like smoothers; Martingale difference sequence; active state tracking; approximate MMSE estimation; body sensing network application; controlled sensing; discrete state Markov chains; innovations approach; nonlinear POMDPs; partially observable Markov decision processes (POMDP); stochastic dynamic programming;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2014.2362098
Filename :
6918519
Link To Document :
بازگشت