Gradient-based target localization in robotic sensor networks

Qingquan Zhang, Gerald E Sobelman, Tian He

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Fast target localization without a map is a challenging problem in search and rescue applications. We propose and evaluate a novel gradient-based method which uses statistical techniques to estimate the position of a stationary target. Mobile nodes can then be directed toward the target using the shortest path. Moreover, localization can be achieved without any assistance from stationary sensor networks. Simulation results demonstrate nearly a 40% reduction in target acquisition time compared to a random walk model. In addition, our method can generate a position prediction map which closely matches the actual distribution in the field. Finally, experiments have been performed using MicaZ motes which further validate our techniques.

Original languageEnglish (US)
Pages (from-to)37-48
Number of pages12
JournalPervasive and Mobile Computing
Volume5
Issue number1
DOIs
StatePublished - Feb 2009

Keywords

  • Gradient-driven
  • Robot
  • Signal strength
  • Target localization
  • Wireless sensor network

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