Posterior CRLB based sensor selection for target tracking in sensor networks

Long Zuo, Ruixin Niu, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

69 Scopus citations

Abstract

The objective in sensor collaboration for target tracking is to dynamically select a subset of sensors over time to optimize tracking performance in terms of mean square error (MSE). In this paper, we apply the Monte Carlo method to compute the expected posterior Cramér-Rao Lower Bound (CRLB) in a nonlinear, possibly non-Gaussian, dynamic system. The joint recursive one-step-ahead CRLB on the state vector is introduced as the criterion for sensor selection. The proposed approach is validated by simulation results. In the experiments, a particle filter is used to track a single target moving according to a white noise acceleration model through a two-dimensional field where bearing-only sensors are randomly distributed. Simulation results demonstrate the improved tracking performance of the proposed method compared to other existing methods in terms of tracking accuracy.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesII1041-II1044
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Extended Kalman Filter (EKF)
  • Particle filters
  • Posterior CRLB
  • Sensor networks
  • Target tracking

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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