本文针对弱机动目标的时间协同打击问题，分别研究了基于匀速运动模型的隐式协同制导问题与基于速度时变模型的显式协同制导问题，以提升反坦克导弹对高价值目标的突防与毁伤能力。首先基于导弹匀速运动假设，采用预测校正的思想与最优误差动力学理论，在比例导引制导律的基础上加入时间约束偏置项，并基于最优误差动力学得到时间约束制导律，从而提出了对通信弱依赖性隐式时间协同制导律。针对速度时变导弹打击时间预测不准确影响隐式协同性能的问题，进一步提出了基于神经网络的显式时间协同制导方法。该方法采用预测校正的制导框架，根据比例导引制导律的特性采用迁移集成神经网络设计预测器以精确预测剩余飞行时间，并提出指定时间收敛的一致性动力学，使导弹在分布式通信架构下协调各自弹道长度，最终保证多枚导弹同时命中目标。仿真结果表明，所提出的隐式时间协同制导方法可保证多枚导弹同时精准命中目标，所设计的基于神经网络的显式时间协同制导方法可保证多枚导弹在变速条件下的协同打击。

This article addresses the problem of time-coordinated strike against weakly maneuvering targets. The implicit cooperative guidance problem based on the constant velocity motion model and the explicit cooperative guidance problem based on the velocity time-varying model are studied to enhance the breakthrough and destruction capabilities of anti-tank missiles against high-value targets. Firstly， based on the assumption of missile motion at a constant velocity， the idea of predictive correction and the optimal error dynamics theory are employed. Time-constrained bias terms are added to the proportional navigation guidance law， and a time-constrained guidance law is derived based on optimal error dynamics. An implicit time-coordinated guidance law scenarios depending weakly on communication is then proposed. To address the issue that inaccurate time prediction affects the performance of implicit cooperation caused by variable speed missile strikes， a neural network-based explicit time-coordinated guidance method is further proposed. This method adopts the predictive correction guidance framework， and designs the predictor by using transfer learning integrated neural networks according to the characteristics of proportional navigation guidance law to accurately predict the remaining flight time. Consistency dynamics with specified time convergence is proposed to coordinate the lengths of missile trajectories in the distributed communication architecture， ensuring that multiple missiles simultaneously hit the target. Simulation results show that the proposed implicit time-coordinated guidance method can ensure that multiple missiles simultaneously hit the target accurately， and the neural network-based explicit time-coordinated guidance method proposed can ensure coordinated strikes against multiple missiles under variable speed conditions.