Abstract:

Based on the novel idea of assigning different weights for the same sample by different classifiers according to the classification ability and assigning different weights for different samples by the same classifier according to the separable degree of samples respectively, a self-adaptive weighted majority vote strategy based on entropy for high range resolution profile (HRRP) fusion recognition is proposed. The multiclass relevance vector machine (MRVM) probabilities model is extended based on the basic RVM model, and three different MRVMs is used to classify different HRRP feature samples, then entropy calculated by the posterior probability of different MRVMs is used to assign weight adaptively, so that different classifiers and the same classifier occupy different proportions in decisionmaking for different samples, low weight is assigned to sample with high entropy. Finally, the weighted majority vote strategy is used to fusion different feature classified results and get the final target recognition results. Experiment results based on simulated data show the efficiency of the proposed method.