## 1 Introduction  
The unmanned aerial vehicle (UAV) has gradually gained popularity in the last few years. In the beginning, UAVs were mainly used for military purposes, and now they are increasingly appearing in the fields of industry and academic research. In terms of autonomous flight research of UAVs, [1] proposes the development of a camera-based quadrotor positioning system to finish the process of automated landing. Reference [2] studies the autonomous navigation system for small UAVs suitable for GPS-denied environment, and realizes the collision-free navigation in fully autonomous missions. The state feedback linear quadratic regulator (LQR) controller is applied to the quadrotor UAV to realize the complete autonomy of the control system [3]. Due to limitations of the load and battery capacity of a single UAV, it is difficult to complete some tasks in complex environments. In recent years, UAV swarms have developed rapidly due to their potential huge application value [4]. UAV swarms show the group behavior through the local interaction between individuals, to solve the global collaborative task [5]. Meanwhile, the current trend of electronic countermeasures (ECM) is moving towards intelligent and distributed direction. Compared with traditional centralized ECM, distributed ECM takes advantage of its number and space to achieve better jamming with targets [6]. The background of distributed ECM and the key technical challenges can be referred to in [7]. In [8], the influence of distributed suppression and deception jammingon radar detection performance is studied. It investigates the jamming effect of a dense UAV swarm on the detection performance of air defense radar. The optimization for the position of each individual through sparse array synthesis is addressed in [9]. However, these studies only demonstrate the effectiveness of distributed ECM, and there is still a lack of corresponding theoretical modeling for analysis on the evolution mechanism of distributed ECM. The cellular automata (CA) has been used to model the propagation and emergence of cluster behavior in complex systems [10]. The traditional modeling method is based on the differential equations and is studied by deductive reasoning, while the CA model simulates and predicts the overall dynamic process. The CA model has been widely used in many fields such as computer network modeling and communication [11; 12; 13]. In the computer field, the side profile flow calculation of sand-like particles in video games is researched in [11]. The CA model can also be used to model computer networks [12]. Reference [13] studies a node scheduling algorithm based on CA, which saves the energy of sensor nodes while ensuring communication coverage. However, there is no literature addressing the application of CA in the field of ECM. Given these gaps in this research, the main contributions of this paper are summarized as follows:  
(a) The process of agent state transition is explored, in which the all selection (AS) strategy of the traditional CA model is improved based on the neighborhood selection (NS) strategy; (b) On the basis of (a), the convergence performance of the model in the four evolution strategies is analyzed; (c) Electromagnetic agents (EA) are UAVs that carry electronic reconnaissance or jamming equipment. By regarding each as a cell, the electromagnetic agent-cellular automata (EA-CA) model is proposed. It is used to simulate the process of cluster jamming in distributed ECM scenarios; (d) By adjusting the parameters of the simulation, the conditions for the system to realize the emergence jamming are analyzed. The rest of this paper is organized as follows: Section 2 describes the state transition process of CA based on the neighborhood selection-all selection (NS-AS) strategy. The application of the EA-CA model under ECM scenario is introduced in Section 3. In Section 4, the performance of the model and the ECM simulation results are provided to demonstrate the effectiveness of proposed model. Concluding remarks are given in Section 5.