Angle only target tracking using a continuous-valued Bayesian network

Eric Driver, Darryl Morrell

Research output: Contribution to journalConference article

Abstract

In this paper, we apply a continuous-valued Bayesian network to the problem of tracking a maneuvering target using only bearing data from a single observer. The resulting tracking algorithm computes an approximate posterior probability density of the target position and velocity given the observations. This algorithm is more robust than typical approaches based on the extended Kalman filter and provides a framework in which side information, such as bounds on target velocity, can be incorporated directly into the estimate. The algorithm's performance is characterized using Monte Carlo simulation.

Original languageEnglish (US)
Pages (from-to)839-843
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume2
StatePublished - Jan 1 1997
Externally publishedYes
EventProceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2) - Pacific Grove, CA, USA
Duration: Nov 3 1996Nov 6 1996

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Angle only target tracking using a continuous-valued Bayesian network'. Together they form a unique fingerprint.

  • Cite this