18,732 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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks
In this paper we study the problem of tracking an object moving randomly
through a network of wireless sensors. Our objective is to devise strategies
for scheduling the sensors to optimize the tradeoff between tracking
performance and energy consumption. We cast the scheduling problem as a
Partially Observable Markov Decision Process (POMDP), where the control actions
correspond to the set of sensors to activate at each time step. Using a
bottom-up approach, we consider different sensing, motion and cost models with
increasing levels of difficulty. At the first level, the sensing regions of the
different sensors do not overlap and the target is only observed within the
sensing range of an active sensor. Then, we consider sensors with overlapping
sensing range such that the tracking error, and hence the actions of the
different sensors, are tightly coupled. Finally, we consider scenarios wherein
the target locations and sensors' observations assume values on continuous
spaces. Exact solutions are generally intractable even for the simplest models
due to the dimensionality of the information and action spaces. Hence, we
devise approximate solution techniques, and in some cases derive lower bounds
on the optimal tradeoff curves. The generated scheduling policies, albeit
suboptimal, often provide close-to-optimal energy-tracking tradeoffs
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Sensor Management for Tracking in Sensor Networks
We study the problem of tracking an object moving through a network of
wireless sensors. In order to conserve energy, the sensors may be put into a
sleep mode with a timer that determines their sleep duration. It is assumed
that an asleep sensor cannot be communicated with or woken up, and hence the
sleep duration needs to be determined at the time the sensor goes to sleep
based on all the information available to the sensor. Having sleeping sensors
in the network could result in degraded tracking performance, therefore, there
is a tradeoff between energy usage and tracking performance. We design sleeping
policies that attempt to optimize this tradeoff and characterize their
performance. As an extension to our previous work in this area [1], we consider
generalized models for object movement, object sensing, and tracking cost. For
discrete state spaces and continuous Gaussian observations, we derive a lower
bound on the optimal energy-tracking tradeoff. It is shown that in the low
tracking error regime, the generated policies approach the derived lower bound
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