A method of ship formation recognition is proposed based on the information fusion of spaceborne IMINT (image
intelligence) and ELINT (electronic intelligence) in this paper. Firstly, the composition and the battle array of the
observed ship formation are derived from the spaceborne IMINT information of each individual ship. The beliefs about
the composition and the battle array of the observed formation are calculated separately. A synthetic evaluation method
is used to get the BPAF (basic probability assignment function) from IMINT information using the aforementioned two
beliefs. Secondly, by computing the matching measure between the observed emitter set and that of the typical formation
entity, the BPAF based on ELINT information is obtained. Thirdly, the two BPAFs based on the information of
spaceborne IMINT and ELINT are combined with DS (Dempster-Shafer) evidence theory. Finally, a decision is made
according to the combined BPAF. The experiment indicates that our proposed methods to obtain the BPAFs are
practicable and the formation recognition accuracy is greatly improved compared to the results which use only one of the
two sources.
In this paper, we propose a whole scheme of remote sensing image segmentation process, from fast detection to accurate
edge location. As we know, more structure information is acquired in high resolution remote sensing images. However,
traditional image processing algorithms will produce meaningless results without priori knowledge. We aim at solving
the problem in which regions may be distinguishable in intensity but belong to the same target by the ground truth. This
is done by multi-threshold segmentation. What's more, In order to get a more regular shape, we use random field model
to introduce spatial constraint at a small scale, and active contour model to smooth the whole edge at a larger scale.
Simulation results demonstrate the effectiveness of our method in extracting ships from the satellite images. This paper
also introduces the potential of integrating the image segmentation and subsequent image analysis tasks.
This paper researches on the method of situation assessment for the air combat based on the Bayesian networks technology. It analyzes the events occur in the process of air combat, and presents a hybrid method of fuzzy sets and Bayesian networks to detect these events. Then, it presents a method to construct Bayesian networks using the events and then uses the networks to reason the purpose of enemy fighter pilots. Finally, it shows the method by an illustrative example.
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