In the field of track initiation, considering the problem of false tracks caused by a large number of residual clutters in complex electromagnetic environment, a new clutter suppression method is proposed.This method is based on Adaptive Boosting (AdaBoost) algorithm, which transforms the clutter suppression problem into that of AdaBoost decision tree classification.First, the base learner is trained to get a rough classification result.On this basis, the results of multiple base learners are combined to classify more comprehensively, and the problem that the weak learner performs poorly on clutter points with insignificant features is solved.The experimental results show that, compared with SVM and KNN classifiers,the proposed method can suppress the residual clutter and reduce its influence on the track initiation more effectively.