Multispectral data fusion for target classification

S. Momprive; Gerard Favier; Marc Ducoulombier

doi:10.1117/12.327129

17 July 1998 Multispectral data fusion for target classification

S. Momprive, Gerard Favier, Marc Ducoulombier

Proceedings Volume 3374, Signal Processing, Sensor Fusion, and Target Recognition VII; (1998) https://doi.org/10.1117/12.327129
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The recognition of targets incoming the surroundings of a ship and detected by an InfraRed Search and Track (IRST) system, is made difficult by the low signal to noise ratio of the data. It results from the requirement to classify targets which are still far enough to permit combat system activation if a threat is identified. Thus, exploiting as much information as available is necessary to increase the robustness of the classification performances. But the combination of multiple information sources leads to an issue of heterogeneous data fusion. Moreover, a consequence of using a passive system is that the range from an unknown target can't be assessed easily, and therefore nor his trajectory. In such a configuration, it's difficult to figure out from which aspect the target is seen, which makes the observed features much less discriminating. This paper describes a new processing architecture which aims at overcoming this difficulty by evaluating, in the frame of the Dempster-Shafer (DS) theory, the likelihood of compound hypothesis consisting of a target class and an aspect angle.

Citation Download Citation

S. Momprive, Gerard Favier, and Marc Ducoulombier "Multispectral data fusion for target classification", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); https://doi.org/10.1117/12.327129

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