The number of unmanned equipment is generally large.Remaining Useful Life (RUL) prediction is especially important due to long mission time and harsh environments.The prediction accuracy of comprehensive performance indicators sequence using a single model is low.In order to solve this problem,an RUL prediction method based on Kalman fusion model is proposed.Firstly,the area maximum method is used to extract the degradation phase of the comprehensive performance indicators of key components of unmanned equipment.Secondly,the GM(1,1) model with exponential characteristics,the linear support vector machine SVR model,and the nonlinear Extreme Learning Machine (ELM) model are used to predict the comprehensive performance indicators.Each model can capture different characteristics of the comprehensive performance indicators.Finally,the Kalman framework is used to fuse the prediction results of the three models according to the principle of iterative least squares.The experimental results show that the prediction method of Kalman fusion model can significantly improve the prediction accuracy of comprehensive performance indicators.Compared with that of single models of ELM,SVR and GM(1,1),the fitting accuracy is increased by 16.96%,1.61% and 39.84% respectively,and the prediction accuracy is increased by 45.06%,38.35% and 74.12% respectively.