Unscented Particle Filter for Tracking a Magnetic Dipole Target

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Authors
  1. Birsan, M.
Corporate Authors
Defence R&D Canada - Atlantic, Dartmouth NS (CAN)
Abstract
In this paper we present a recursive Bayesian solution to the problem of joint tracking and classification of a vessel 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 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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Keywords
recursive Bayesian;tracking and classification;Kalman filter
Report Number
DRDC-ATLANTIC-SL-2005-199 — Scientific Literature
Date of publication
19 Sep 2005
Number of Pages
4
DSTKIM No
CA026388
CANDIS No
524251
Format(s):
Electronic Document(PDF)

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