In order to explore a more applicable target allocation method for anti-missile operation， the connotation of target allocation model in multi-layer anti-missile cooperative operation is defined， and the basic idea of constructing the target allocation model of multi-layer anti-missile cooperative operation is clarified. The index system of ballistic missile threat assessment， and feasibility analysis of combat unit interception are established respectively. The ballistic missile threat assessment model and the strength analysis model of combat unit interception are established through normalization and weight assignment. On this basis， combined with the actual requirements of multi-layer and anti-missile cooperative combat firepower application， the Hungarian algorithm is improved based on vitality transfer， the target allocation model of multi-layer anti-missile cooperative operation based on improved Hungarian algorithm is constructed. And the model is used to simulate the case with the background of multi-layer anti-missile combat scenario to verify the rationality and effectiveness of the model.

In order to achieve effective destruction of key parts of the warship， such as bridge and vertical launcher， an adaptive detonation strategy for anti-air missile attacking warships is proposed. Taking the Arleigh Burke class destroyer as a typical target， based on the characteristics of the anti-air missile attacking warships， an adaptive detonation control model consisting of trigger initiation， distance initiation and delayed limited initiation is established by making full use of the information on the missile. The damage criterion of anti-air missile attacking warships is established， and the initiation conditions and damage effect， under different rendezvous condition are studied through simulation calculation， and the effectiveness of adaptive detonation strategy is verified， which provides technical support for expanding the anti-ship combat capability of anti-air missile and improving the efficiency of weapons.