A Direct Algorithm for Joint Optimal Sensor Scheduling and MAP State Estimation for Hidden Markov Models
David Jun, David Cohen, Douglas L. Jones

Citation
David Jun, David Cohen, Douglas L. Jones. "A Direct Algorithm for Joint Optimal Sensor Scheduling and MAP State Estimation for Hidden Markov Models". IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May, 2013.

Abstract
Sensing systems with multiple sensors and operating modes warrant active management techniques to balance estimation quality and measurement costs. Existing literature shows that in the joint sensor-scheduling and state-estimation problem for HMMs, estimator optimization can be done independently of the scheduler at each time step. In this paper, the special case of using a MAP estimator is investigated, and it is shown how the joint problem can be converted to a standard Partially Observable Markov Decision Process (POMDP), enabling the use of POMDP solvers. As this approach is highly redundant, a direct solution is derived, which exploits the separability property while still utilizing standard solvers. When compared to standard techniques, the direct algorithm provides savings by a factor of the state-space dimension. Numerical results are given for an example motivated by wildlife monitoring.

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Citation formats

• HTML

  David Jun, David Cohen, Douglas L. Jones. <a
  href="http://www.terraswarm.org/pubs/55.html"
  >A Direct Algorithm for Joint Optimal Sensor Scheduling
  and MAP State Estimation for Hidden Markov Models</a>,
  IEEE International Conference on Acoustics, Speech and
  Signal Processing (ICASSP), May, 2013.

• Plain text

  David Jun, David Cohen, Douglas L. Jones. "A Direct
  Algorithm for Joint Optimal Sensor Scheduling and MAP State
  Estimation for Hidden Markov Models". IEEE
  International Conference on Acoustics, Speech and Signal
  Processing (ICASSP), May, 2013.

• BibTeX

  @inproceedings{JunCohenJones13_DirectAlgorithmForJointOptimalSensorSchedulingMAPState,
      author = {David Jun and David Cohen and Douglas L. Jones},
      title = {A Direct Algorithm for Joint Optimal Sensor
                Scheduling and MAP State Estimation for Hidden
                Markov Models},
      booktitle = {IEEE International Conference on Acoustics, Speech
                and Signal Processing (ICASSP)},
      month = {May},
      year = {2013},
      abstract = {Sensing systems with multiple sensors and
                operating modes warrant active management
                techniques to balance estimation quality and
                measurement costs. Existing literature shows that
                in the joint sensor-scheduling and
                state-estimation problem for HMMs, estimator
                optimization can be done independently of the
                scheduler at each time step. In this paper, the
                special case of using a MAP estimator is
                investigated, and it is shown how the joint
                problem can be converted to a standard Partially
                Observable Markov Decision Process (POMDP),
                enabling the use of POMDP solvers. As this
                approach is highly redundant, a direct solution is
                derived, which exploits the separability property
                while still utilizing standard solvers. When
                compared to standard techniques, the direct
                algorithm provides savings by a factor of the
                state-space dimension. Numerical results are given
                for an example motivated by wildlife monitoring.},
      URL = {http://terraswarm.org/pubs/55.html}
  }
  

Posted by David Jun on 28 Apr 2013.
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