Nonmyopic sensor scheduling and its efficient implementation for target tracking applications

  • Authors:

  • Amit S. Chhetri;Darryl Morrell;Antonia Papandreou-Suppappola

  • Affiliations:

  • Department of Electrical Engineering, Arizona State University, Tempe, AZ;Department of Engineering, Arizona State University, Tempe, AZ;Department of Electrical Engineering, Arizona State University, Tempe, AZ

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2006

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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.