QoI-based Resource Allocation for Multi-Target Tracking in Energy Constrained Sensor Networks

Abstract

Motivated by the need to judiciously allocate scarce sensing resources, and account for fusing information from multi-modal sensors, we developed a solutions methodology for maximizing the overall Quality of Information (QoI) obtained subject to constraints on the energy utilized by a sensor network that is involved in the task of tracking multiple targets. Our methodology is based on integer programming, and explicitly allows for general fusion functions. We use an iterative Lagrangian relaxation technique to solve this problem, whereby each iteration step involves solving for a Maximum Weight Independent Set (MWIS) of an appropriately constructed graph (which can be obtained in polynomial time for this problem). We apply our methodology to numerically study the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field. In these applications, we study the QoI/energy tradeoffs for various modes of operation, including the period for measurement updates.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2011
Accession Number
ADA562802

Entities

People

  • Chatschik Bisdikian
  • Lance Kaplan
  • Srikanth Hariharan
  • Tien Pham

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Programming
  • Detectors
  • High Energy
  • Integer Programming
  • Iterations
  • Kalman Filters
  • Measurement
  • Military Research
  • Multiple Targets
  • Multitarget Tracking
  • Networks
  • Polynomials
  • Sensor Networks
  • Target Tracking
  • Targets

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Operations Research
  • Sensor Fusion and Tracking Systems.