Directional warhead technology is a new killing technology that uses target miss direction information to change the blasting direction through the change of missile body or the logic control of warhead initiation to achieve the maximum damage effect. It is also one of the key promising technologies for the future new generation of air-defense missiles to achieve capability leaping. The overall design problems of the aimed killing air-defense missile equipped with a directional warhead are studied. Firstly， a prediction method of miss information that compensates the missile acceleration for aimed killing air-defense missiles is proposed. The demand for acquiring miss information in advance is met. Secondly， the distributing regularity of the predicted impacting point and the formation mechanism of fuzzy prediction are studied， which provides theoretical support for solving the fuzzy problem. Thirdly， the aiming accuracy improvement methods for small miss distance encounter cases are studied. Finally， the simulations taking a certain type of aimed killing air-defense missile as examples are conducted.

The key to improving the accuracy of vibration mode identification is to improve the separation accuracy of dense natural modes. Based on the analysis of the sieving results of ensemble empirical mode decomposition （EEMD）， this paper proposes a multi-noise method. The improved sieving algorithm， combined with the Hilbert Transform（HT） method， is applied to the identification of missile operating modes. Compared with the sieving results of empirical mode decomposition and ensemble empirical mode decomposition algorithms， the new algorithm reduces the error and effectively suppresses the phenomenon of modal aliasing. Compared with the logarithmic decay method， the Hilbert transform is used for mode identification， which improves the parameter identification accuracy， and can identify the variation law of natural frequency with time， which is more suitable for analyzing the time-varying missile working mode. This research can provide a reference for solving the modal aliasing problem in signal processing.