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Distributed Estimation and Tracking Using Time-Difference-of-Arrival (TDOA) Measurements

[+] Author Affiliations
Osama N. Ennasr, Xiaobo Tan

Michigan State University, East Lansing, MI

Paper No. DSCC2017-5351, pp. V002T14A011; 8 pages
doi:10.1115/DSCC2017-5351
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

Localization and tracking of a moving target arises in many different contexts and is an important problem in the field of wireless sensor networks. One class of localization schemes exploits the time-difference-of-arrival (TDOA) of a signal emitted by the target and detected by multiple sensors. Much of the existing work on TDOA-based target localization and tracking adopts a centralized approach, where all measurements are sent to a reference agent which produces an estimate of the target’s location. In this work, we propose a fully distributed approach to target localization and tracking by a group of mobile robots. Specifically, we utilize a Networked Extended Kalman Filter (NEKF) to estimate the target’s location in a distributed manner. The target location estimates by individual robots, which are shown to converge to the true value, are then fed into a distributed control law that maintains a specified formation of the robots around the target, which optimizes the estimation accuracy. In order to reduce the energy expenditure of the robots, we further propose a movement control strategy based on the Cramer-Rao bound to balance the trade-off between estimation performance and the total distance traveled by the robots. A numerical example involving robots with unicycle dynamics is provided to illustrate the performance of the proposed approach.

Copyright © 2017 by ASME

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