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Proceedings Paper

Multi-target detection and estimation with the use of massive independent, identical sensors
Author(s): Tiancheng Li; Juan M. Corchado; Javier Bajo; Genshe Chen
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Paper Abstract

This paper investigates the problem of using a large number of independent, identical sensors jointly for multi-object detection and estimation (MODE), namely massive sensor MODE. This is significantly different to the general target tracking using few sensors. The massive sensor data allows very accurate estimation in theory (but may instead go conversely in fact) but will also cause a heavy computational burden for the traditional filter-based tracker. Instead, we propose a clustering method to fuse massive sensor data in the same state space, which is shown to be able to filter clutter and to estimate states of the targets without the use of any traditional filter. This non-Bayesian solution as referred to massive sensor observation-only (O2) inference needs neither to assume the target/clutter model nor to know the system noises. Therefore it can handle challenging scenarios with few prior information and do so very fast computationally. Simulations with the use of massive homogeneous (independent identical distributed) sensors have demonstrated the validity and superiority of the proposed approach.

Paper Details

Date Published: 22 May 2015
PDF: 10 pages
Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 94690G (22 May 2015); doi: 10.1117/12.2177973
Show Author Affiliations
Tiancheng Li, Univ. of Salamanca (Spain)
Juan M. Corchado, Univ. of Salamanca (Spain)
Javier Bajo, Univ. Politécnica de Madrid (Spain)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)


Published in SPIE Proceedings Vol. 9469:
Sensors and Systems for Space Applications VIII
Khanh D. Pham; Genshe Chen, Editor(s)

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