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Nonmyopic Sensor Scheduling and its Efficient Implementation for Target Tracking Applications

  • Amit S ChhetriEmail author
  • Darryl Morrell
  • Antonia Papandreou-Suppappola
Open Access
Research Article

Abstract

We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearing-only sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.

Keywords

Covariance Schedule Algorithm Tracker Error Particle Filter Target Tracking 

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Copyright information

© Chhetri et al. 2006

Authors and Affiliations

  • Amit S Chhetri
    • 1
    Email author
  • Darryl Morrell
    • 2
  • Antonia Papandreou-Suppappola
    • 1
  1. 1.Department of Electrical EngineeringArizona State UniversityTempeUSA
  2. 2.Department of EngineeringArizona State UniversityTempeUSA

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