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Proceedings Paper

Distributed multisensor multitarget tracking algorithm
Author(s): Ying Zhang; Henry Leung; Titus K. Y. Lo; John Litva
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Paper Abstract

In this paper, an efficient distributed multi-sensor multi-target tracking algorithm is proposed. This distributed tracker consists of two main components: local sensor-level trackers and a track fuser. In the track fuser, track data from local sensors are first transformed to a common coordinate, and synchronized using a linear Kalman filter. A sequential minimum normalized distance nearest neighbor correlation and minimum mean-square error fusion algorithm, combined with the majority decision making logic is presented to correlate and fuse tracks from different sensors. Simulated data under various tracking conditions are used to evaluate the feasibility and effectiveness of this new distributed tracker.

Paper Details

Date Published: 5 July 1995
PDF: 13 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213010
Show Author Affiliations
Ying Zhang, McMaster Univ. (Canada)
Henry Leung, Deference Research Establishment Ottawa (Canada)
Titus K. Y. Lo, McMaster Univ. (Canada)
John Litva, McMaster Univ. (Canada)


Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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