17 July 1998 Multispectral data fusion for target classification
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Momprive, Gerard Favier, Marc Ducoulombier, "Multispectral data fusion for target classification", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327129; https://doi.org/10.1117/12.327129
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