Abstract: For the multi-function radar (MFR) emitter identification with incomplete data, aiming at the problems of prior knowledge demand, low accuracy and poor robustness, which exist in the conventional identification methods, a method of waveform unit identification based on incomplete prior knowledge set is proposed. Firstly, based on the MFR waveform unit identification framework, the original prior knowledge with parameter missing is multiply divided, and a number of subsets of samples without parameter missing are obtained. Secondly, the redundant subsets are removed and a weak classifier is constructed for each subset by using the random forest algorithm. Finally, the weight is set according to the contribution rate of each weak classifier to the identification result, and the final identification model is obtained. Simulation results confirm the validity of the MDRF-WA waveform unit identification method proposed, moreover, MDRF-WA method can make full use of known prior knowledge, reduce the computational cost and improve the robustness and accuracy of the waveform unit identification under the condition of small training samples.