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Sensor information monotonicity in disambiguation protocols

Author

Listed:
  • X Ye

    (Johns Hopkins University)

  • D E Fishkind

    (Johns Hopkins University)

  • L Abrams

    (George Washington University)

  • C E Priebe

    (Johns Hopkins University)

Abstract

Previous work has considered the problem of swiftly traversing a marked traversal-medium where the marks represent probabilities that associated local regions are traversable, further supposing that the traverser is equipped with a dynamic capability to disambiguate these regions en route. In practice, however, the marks are given by a noisy sensor, and are only estimates of the respective probabilities of traversability. In this paper, we investigate the performance of disambiguation protocols that utilize such sensor readings. In particular, we investigate the difference in performance when a disambiguation protocol employs various sensors ranked by their estimation quality. We demonstrate that a superior sensor can yield superior traversal performance—so called Sensor Information Monotonicity. In so doing, we provide to the decision-maker the wherewithal to quantitatively assess the advantage of a superior (and presumably more expensive) sensor in light of the associated improvement in performance.

Suggested Citation

  • X Ye & D E Fishkind & L Abrams & C E Priebe, 2011. "Sensor information monotonicity in disambiguation protocols," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 142-151, January.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:1:d:10.1057_jors.2009.152
    DOI: 10.1057/jors.2009.152
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    References listed on IDEAS

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    1. Carey E. Priebe & Donniell E. Fishkind & Lowell Abrams & Christine D. Piatko, 2005. "Random disambiguation paths for traversing a mapped hazard field," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 285-292, April.
    2. Priebe, Carey E. & Naiman, Daniel Q. & Cope, Leslie M., 2001. "Importance sampling for spatial scan analysis: computing scan statistic p-values for marked point processes," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 475-485, February.
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    Cited by:

    1. Vural Aksakalli & Ibrahim Ari, 2014. "Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 370-384, May.

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