Abstract: In this work we present a new process distribution approach for sensordatafusion (SDF) systems, called FuseDis (fusion distribution using tessellated space). It is based on a hybrid partitioning approach, aimed at managing computational burden and achieving scalability.
First, a functional decomposition dicides SDF functionality into taskgroups, vectorizing operations. Second, a partitioning of the dataspace, based on geographic attributes of the data, is applied to parallelize the processing.
A tesselation applied to the plot-space imlicitly defines for each tile the set of candidate-tracks yielding useful correlations with the plots in a tile. Some tracks may occur as correlation-candidates for multiple tiles. Conflicts caused by correlations of such tracks with plots in different tiles are solved by combining correlations of the involved tracks and plots into independent association problems
Abstract: French coastguard missions have become increasingly varied implying new challenges such as the reduction of the decision cycle and the expansion of available information. Thus, it involves new needs for enhanced decision support. An efficient situation awareness system has to quickly detect and identify suspicious boats. The efficiency of such a system relies on a reliable sensorfusion since a coastguard uses sensors to achieve his mission. We present an innovative approach based on multi-agent negotiation to fuse classifiers, benefiting from the efficiency of existing classification tools and from the flexibility and reliability of a multi-agent system to exploit distributed data across dispersed sources. We developed a first prototype using a basic negotiation protocol in order to validate the feasibility and the interest of our approach. The results obtained are promising and encourage us to continue on this way.