Share Email Print

Optical Engineering

Neural network data association with application to multiple-target tracking
Author(s): Henry Leung
Format Member Price Non-Member Price
PDF $20.00 $25.00
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

Data association is the process of relating sensor measurements in a data fusion system. It can be structured in a basic framework very similar to that of the classic traveling salesman problem. The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge–Kutta method and Aiyer’s network structure. The neural data association is then applied to the problem of multiple-target tracking (MTT). The proposed neural MTT system consists of a modified Hough transform track initiator, a Kalman filter state estimator and the Hopfield probabilistic data association. Real-life air surveillance data are used to evaluate the practicality of the neural MTT system, and the results show that the neural system works efficiently in real-life tracking environments.

Paper Details

Date Published: 1 March 1996
PDF: 8 pages
Opt. Eng. 35(3) doi: 10.1117/1.600661
Published in: Optical Engineering Volume 35, Issue 3
Show Author Affiliations
Henry Leung, Defence Research Establishment Ottawa (Canada)

© SPIE. Terms of Use
Back to Top