سید عباس ساداتی نژاد

An automatic guidance system in drones can be divided into two parts: path design and follow the path. In these systems, due to the time limitations and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of the military commanders and emulate their behavior in these systems, due to the time constraints and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of military commanders and simulate their behavior in the design and route tracking processes. Therefore, according to the characteristics of fuzzy inference systems, by using them, the commanders' expertise can be applied in autonomous drones. In this article, in order to model the UAVs offensive routing, a Mamdani fuzzy inference system with five inputs and one output is used to determine the edges weight. At each stage of decision-making process of choosing the optimal route, the need to perform complex mathematical calculations can make drone routing algorithms useless in real-world conditions. Therefore, in order to reduce the dependence of the routing and target tracking system on mathematical calculations and to use the advantages of anytime algorithms to produce the optimal answer, genetic algorithm and non-dominant sorting genetic algorithm-II have been used as the method of solving the model. The simulation results show that the combination of genetic algorithm and fuzzy inference system has a very favorable efficiency in performing the routing process in real world conditions and can meet the operational needs of commanders in this field.

Today, the threats caused by micro-birds are one of the most important challenges of defense systems. The two main components in managing these threats are modeling and decision-making based on the model drawn from them. Determining the optimal path for the movement of small birds is one of the most important decisions in this field. In this process, the routing is done based on the modeling done from the assessment of the space of states and available resources. In this regard, in this article, in order to depict the situation more accurately, the sequential nature of the problem of the allocation of small birds in offensive operations is considered. Therefore, in this article, a multi-objective-multi-stage model for determining the weight of edges in the routing graph is presented. Also, genetic algorithm and genetic algorithm with non-dominant sorting are used to find the optimal routing for operations in the battle scene. The efficiency of the genetic algorithm with non-dominant sorting is evaluated using three criteria: generation distance as a convergence criterion, extent as a diversity criterion, and actual computation time.

Nowadays, the threats caused by aerial targets are among the most critical challenges. Two main components in threat management are visualization and decision-making based on compiled pictures. In this process, the decisions are made based on threat evaluation and resource allocation modelling. To do so, the sequential nature of resource allocation is considered to provide a more accurately visualized situation. Here, a multi-stage multi-objective model is constructed based on the fuzzy inference system and the decision tree. Also, NSGA-II and SPEA-II are used to find Pareto solutions. Both Algorithms have been compared based on generational distance as a convergence measure, spread as a diversity measure, and the actual computational time. Ultimately, the TOPSIS method is used to make the final decision while the results are reported using a simulated scenario. The simulation results show that the SPEA-II has better convergence and spread, while NSGA-II is faster and has less standard deviation in the execution time. We believe that the fuzzy inference system is more suitable than the decision tree in practical applications. Nevertheless, when facing data shortage or incompatibility, the decision tree would be the preferred choice.