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Track classification within wireless sensor network
Author(s): Robin Doumerc; Benjamin Pannetier; Julien Moras; Jean Dezert; Loic Canevet
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Paper Abstract

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation (“hunter hunt” scenario).

Paper Details

Date Published: 4 May 2017
PDF: 20 pages
Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900T (4 May 2017); doi: 10.1117/12.2267990
Show Author Affiliations
Robin Doumerc, ONERA (France)
Benjamin Pannetier, ONERA (France)
Julien Moras, ONERA (France)
Jean Dezert, ONERA (France)
Loic Canevet, DGA Techniques Terrestres (France)


Published in SPIE Proceedings Vol. 10190:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
Tien Pham; Michael A. Kolodny, Editor(s)

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