Unscented Particle Filter for Tracking a Magnetic Dipole Target

Abstract

In this paper we present a recursive Bayesian solution to the problem of joint tracking and classification of a target modeled at a distance by an equivalent magnetic dipole. Tracking/classification of a magnetic dipole from noisy magnetic field measurements involves the modeling of a non-linear non-Gaussian system. This system allows for complications due to multiple directions of arrival and target maneuver. The determination of target position, velocity and magnetic moment is formulated as an optimal stochastic estimation problem, which could be solved using a sequential Monte Carlo based approach known as the particle filter. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses the unscented Kalman filter (UKF) to generate the transition prior as the proposal distribution.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA439870

Entities

People

  • Marius Birsan

Organizations

  • Defence Research and Development Canada

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computational Science
  • Dipole Moments
  • Dipoles
  • Filters
  • Filtration
  • Kalman Filters
  • Magnetic Dipoles
  • Magnetic Fields
  • Magnetic Moments
  • Mathematical Filters
  • Measurement
  • Monte Carlo Method
  • Particles
  • Sequential Monte Carlo Methods
  • Statistical Algorithms

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Superconducting Magnet Technology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms