A multi-feature joint method for radar pulse repetition interval （PRI） modulation type recognition is proposed to address the challenges of low recognition accuracy， significant impacts from pulse loss and false pulses， and sensitivity to decision threshold settings in existing types. First， the concept of density clustering is employed to remove irrelevant pulses， enabling the preprocessing of the pulse train and mitigating the effects of pulse loss and spurious pulses on pulse train patterns. Statistical methods are then applied to extract the stability features， second-order differential variation features， continuity features and pulse pair matching features of signal time difference. Finally， a random forest algorithm is used to train and test the extracted features. The simulation results demonstrate that the proposed method achieves high recognition accuracy for PRI modulation types and exhibits robust resistance to pulse loss and spurious pulses.

With the continuous improvement of war informatization， systematization and intelligence， the demand of military users for missile equipment support has changed from post maintenance and regular maintenance to condition-based maintenance and intelligent support， which puts forward higher requirements for missile equipment health management. Through the demand analysis of missile equipment health management system， this paper puts forward the overall system architecture for missile equipment support， the system physical architecture for complex application scenarios in military plants and the system technical architecture based on micro-service， and carries out the system function modules by using structural design method， describes the key technologies of the system， constructes and applies the first phase of the system for the pilot equipment. The application results show the feasibility of this study， which can provide reference for the health management of other types of equipment.