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A nonsingular distributed cooperative terminal guidance law is proposed for the problem of multiple missiles attacking ground stationary target simultaneously. By selecting the estimated attack time as consistency variable, the consistency algorithm is combined with nonsingular guidance law, and the limitation caused by the assumption of small angle and specified attack time can be avoided. The closed-loop stabilities of the consensus error and the lead angle are proved based on Lyapunov stability theory, which can verify the effectiveness of the proposed distributed cooperative guidance law. Numerical contrast simulations further verify that the miss distance of the designed guidance law under large initial lead angle is one order of magnitude smaller than that of the compared distributed guidance law, demonstrating the effectiveness and superiority of the proposed guidance law.

Aiming at the problem of multi-missile cooperative interception of maneuvering targets, two different cooperative guidance laws are designed based on the time-to-go. For the scenario of multi-missile distributed communication, a distributed cooperative proportional guidance law with time-varying navigation coefficients is designed based on the idea of biased proportional guidance. For the scenario of specified attack time, based on the form of time-to-go under constant lead angle, a sliding mode guidance law with a specified attack time is designed by using sliding mode control and nonlinear disturbance observer. Simulation results show that the two guidance laws proposed in this paper can effectively carry out cooperative attack or sequential attack against maneuvering targets.

Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions. [ABSTRACT FROM AUTHOR]