Distributed Combat System of Systems Network Modeling

Abstract

<p class="Abstract"><span lang="EN-US">How to generate the topology model of Distributed combat System of Systems network is an important issue in combat analysis. A combat network construction algorithm was proposed to solve the problem. The improved hierarchy network evolving method was used to construct the command and control network, and the combat network generation algorithm was developed by the growth and local priority connections of new node joining into the command network. </span><span lang="EN-US">And then the analytical expression of the degree distribution of the network model was deduced via the </span><span lang="EN-GB">mean-field theory method</span><span lang="EN-US">. </span><span lang="EN-US">Finally, the network model was analyzed according to the topology statistical parameters. The analyzing results show that under the same command span, though the command network topology doesn&rsquo;t change when command level was increased, but the topology performance of the combat network is improved. This is in line with actual combat network</span><span lang="EN-US">; the comparison of degree distribution of analytical results and simulation results indicated that the degree distribution of network model we proposed follows a power law distribution, with exponential value depending on the initial number of command and control network and the number of nodes connected to the rest of the network , </span><span lang="EN-US">verifying the validity of the algorithm model</span><span lang="EN-US">.</span></p

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

“…The rapid development of network science brings new hope. Much research has been done on complex network representations of military organizations [17], [18], [19].…”

“…The membership degree of the ability cluster is obtained by using the membership degree function to form a set. To solve the objective function according to the calculation expression (18), each ability will be divided into the set of the ability cluster with the highest membership degree.…”

“…Then the number of capability clusters is determined and the membership matrix is obtained by using the objective function M (U, V ). Then, according to the calculation expression (18), the ZX of the ability cluster center is calculated. Then, according to the demand, the objective function is used to calculate the value of the objective function of the ability cluster center, and the membership FIGURE 6: Capacity requirements and task mapping matrix is updated until the clustering requirements are met.…”