Multi-spectral data fusion for target classification

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 a 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.