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.

Electronic downloads


Internal. This publication has been marked by the author for TerraSwarm-only distribution, so electronic downloads are not available without logging in.
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.
Groups: services

Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.