A track initiation approach based on random forests with generalized entropy has been proposed in this paper.In the framework of generalized entropy,the generalization performance of decision tree in random forest is improved on various data sets by introducing an adjustable parameter.Furthermore,it can effectively over-come the problems of poor adaptability to data and local optimization in the process of tree building.In this work,the radar target track initiation problem has been transformed into sample classification with supervised learning.In the first stage,the random forest decision tree model has been constructed based on historical data.Then the measurement data are preprocessed using the generalized entropy adjustable simple rule method to get the test samples.Finally,the test samples will be input the trained random forest to get the target track initi-ation result.It is verified by radar data that the proposed method has better track initiation performance com-pared with the traditional random forest method.
维普
万方数据