Tracking a moving object with a binary sensor network

  • Authors:

  • Javed Aslam;Zack Butler;Florin Constantin;Valentino Crespi;George Cybenko;Daniela Rus

  • Affiliations:

  • Northeastern University;Dartmouth College;Dartmouth College;California State University Los Angeles;Dartmouth College;Dartmouth College

  • Venue:
  • Proceedings of the 1st international conference on Embedded networked sensor systems
  • Year:
  • 2003

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Abstract

In this paper we examine the role of very simple and noisy sensors for the tracking problem. We propose a binary sensor model, where each sensor's value is converted reliably to one bit of information only: whether the object is moving toward the sensor or away from the sensor. We show that a network of binary sensors has geometric properties that can be used to develop a solution for tracking with binary sensors and present resulting algorithms and simulation experiments. We develop a particle filtering style algorithm for target tracking using such minimalist sensors. We present an analysis of fundamental tracking limitation under this sensor model, and show how this limitation can be overcome through the use of a single bit of proximity information at each sensor node. Our extensive simulations show low error that decreases with sensor density.