Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper, compared to the traditional RELU algorithm, the agent trained with reinforcement learning algorithms, such as Deep Deterministic Policy Gradient algorithm, Asynchronous Advantage Actor-Critic algorithm, Deep Q Network algorithm performs better. It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific. In contrast to other reinforcement learning algorithms, the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation, and the use of weapon resources is more efficient. It shows that the model and algorithm have certain superiority and rationality. The results of this paper provide new ideas for solving the problem of weapon-target assignment in air defense combat command decisions.

With the rapid development of information technology, network-centric warfare (NCW) has become the main operational style now and even in the future, and the destruction resistance of networks has become one of the most important issues in the field. Based on the complex networks theory, the command and control (C2) network is constructed, and the topology characteristics of the network are analyzed. Aiming at the deliberate attack and random attack, the destruction resistance of C2 network is analyzed and optimized. The simulation results show that the C2 network conforms to the small-world and free-scale characteristics, and it is sensitive to deliberate attack. The strategy of low-degree edge addition can be adopted, that is, by expanding the horizontal contact between the same layer of combat entities and increasing the cross-layer contact between the different layers, the destruction resistance of C2 network can be effectively enhanced. The simulation results have a certain theoretical significance for the establishment of the C2 network and the optimization of destruction resistance of C2 network.

Electromagnetic rail launch technology has made impressive progress; however, the analytical method of calculating the inductance gradient for a complex electromagnetic launcher is still insufficient. By fully considering the characteristics of electromagnetics and current distribution in a device, this paper describes a model of the current skin effect by simplifying the line current distribution in the device. Based on Biot–Savart Law, an analytical method of calculating inductance gradient for an electromagnetic rail launcher with complex structure is proposed. This method has the advantages of fast calculation speed and accurate calculation results. Because of error analysis, the calculated value relatively corresponds to the simulation result of the eddy current field. To reflect the transient electromagnetic emission process, the effects of different configurations, current frequency, and armature position on the inductance gradient are further summarized. The results show that the error rate of this method in calculating the inductance gradient is about 4%, which meets the requirement for calculation accuracy. The inductance gradient of the enhanced four-rail electromagnetic launcher is about 2.22 times that of the nonenhanced one due to the equal conditions; the inductance gradient decreases with the increase in current frequency and decreases as the armature approaches the muzzle.

The ablation and wear of the four-rail electromagnetic launcher during the working process will aggravate the damage of the armature and rail, and greatly affect the service life of the launcher. To effectively alleviate rail damage, this paper applies the copper-steel composite rail to the four-rail electromagnetic launcher and proposes a new four-rail electromagnetic launcher. Based on the quadrupole magnetic field theory, the physical model of the new four-rail electromagnetic launcher is established, and the electromagnetic characteristics of the ordinary and new launchers are compared and analyzed using the finite element method. On this basis, the influence of composite layer parameters on the electromagnetic characteristics of copper-steel composite quadrupole rail is explored. The study found that the new four-rail electromagnetic launcher can provide a better launch magnetic field environment for smart loads, and the current distribution of the armature and the rail contact surface is more uniform, which can effectively improve the contact condition between the armature and the rail. The composite layer parameters of copper-based composite rail will have a certain impact on electromagnetic characteristics, and copper-steel composite rail of appropriate proportions can be selected according to different needs. The model proposed in this paper has a certain degree of scientificity and rationality.

During the operation of the Quadrupole Compound Orbital electromagnetic launcher, the current is easy to gather in the armature and the rail contact surface. Serious turn and arc ablation can occur, causing damage to the rail and the armature and affecting the life of the launcher. To better solve the thermal ablation problem of the armature and the rail, three different configurations of the rail and the armature are established, and the current density, magnetic field distribution and electromagnetic force of the rail and the armature are compared and analyzed using the finite element method, and the effect of concave and convex values of the armature rail on current distribution and electromagnetic force is discussed. The results show that the planar armature can effectively reduce the maximum current density and mitigate thermal damage. The concave elliptical rail produces the largest electromagnetic thrust and the smallest radial electromagnetic force, and the armature is more stable during firing. The maximum current density and magnetic field strength are negatively correlated with the concave and convex values; the electromagnetic thrust applied to the concave elliptical armature is negatively correlated with the concave value, while the electromagnetic force applied to the convex elliptical armature is positively correlated with the convex value.

In the existing composite four-rail electromagnetic launcher (CFREL), the armature and rail contact surface produces significant heat and bears considerable wear, thereby reducing the potential amount of electromagnetic thrust to be generated. To eliminate the damage caused by the thermal effect of the rail contact surface and to meet the electromagnetic thrust demand of the load, a CFREL is proposed. The proposed CFREL model is constructed, and the launcher’s electromagnetic characteristics are simulated and compared using the finite element method. The current density, distribution of magnetic flux density, and electromagnetic thrust characteristics are analysed. The results showed that the proposed CFREL reduced the maximum current density of the contact surface and effectively eliminated the current concentration of the armature and rail contact surfaces. Effective magnetic field shielding is realised with a larger range, which can better meet the requirements of the intelligent load’s magnetic field environment and provide stronger electromagnetic thrust for the load, hence solving the problem of insufficient thrust.