67 research outputs found
Active Classification for POMDPs: a Kalman-like State Estimator
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.Comment: 38 pages, 6 figure
Sequential Detection with Mutual Information Stopping Cost
This paper formulates and solves a sequential detection problem that involves
the mutual information (stochastic observability) of a Gaussian process
observed in noise with missing measurements. The main result is that the
optimal decision is characterized by a monotone policy on the partially ordered
set of positive definite covariance matrices. This monotone structure implies
that numerically efficient algorithms can be designed to estimate and implement
monotone parametrized decision policies.The sequential detection problem is
motivated by applications in radar scheduling where the aim is to maintain the
mutual information of all targets within a specified bound. We illustrate the
problem formulation and performance of monotone parametrized policies via
numerical examples in fly-by and persistent-surveillance applications involving
a GMTI (Ground Moving Target Indicator) radar
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