Share Email Print
cover

Proceedings Paper

Novel branching particle method for tracking
Author(s): David J. Ballantyne; Hubert Y. Chan; Michael A. Kouritzin
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Particle approximations are used to track a maneuvering signal given only a noisy, corrupted sequence of observations, as are encountered in target tracking and surveillance. The signal exhibits nonlinearities that preclude the optimal use of a Kalman filter. It obeys a stochastic differential equation (SDE) in a seven-dimensional state space, one dimension of which is a discrete maneuver type. The maneuver type switches as a Markov chain and each maneuver identifies a unique SDE for the propagation of the remaining six state parameters. Observations are constructed at discrete time intervals by projecting a polygon corresponding to the target state onto two dimensions and incorporating the noise. A new branching particle filter is introduced and compared with two existing particle filters. The filters simulate a large number of independent particles, each of which moves with the stochastic law of the target. Particles are weighted, redistributed, or branched, depending on the method of filtering, based on their accordance with the current observation from the sequence. Each filter provides an approximated probability distribution of the target state given all back observations. All three particle filters converge to the exact conditional distribution as the number of particles goes to infinity, but differ in how well they perform with a finite number of particles. Using the exactly known ground truth, the root-mean-squared (RMS) errors in target position of the estimated distributions from the three filters are compared. The relative tracking power of the filters is quantified for this target at varying sizes, particle counts, and levels of observation noise.

Paper Details

Date Published: 13 July 2000
PDF: 11 pages
Proc. SPIE 4048, Signal and Data Processing of Small Targets 2000, (13 July 2000); doi: 10.1117/12.391984
Show Author Affiliations
David J. Ballantyne, Univ. of Alberta (Canada)
Hubert Y. Chan, Univ. of Alberta (Canada)
Michael A. Kouritzin, Univ. of Alberta (Canada)


Published in SPIE Proceedings Vol. 4048:
Signal and Data Processing of Small Targets 2000
Oliver E. Drummond, Editor(s)

© SPIE. Terms of Use
Back to Top