关键词: 室内被动定位, 特征选择, 马尔科夫毯, 支持向量机

Abstract: In the study of radio frequency based indoor passive localization, there may be so many link features that the complexity of classifiers is high. To solve this problem, an indoor passive localization with class-based feature selection method is proposed. To convert a multi-class classification to multiple two-class classifications and use max-min Markov blanket algorithm to select discriminative features for every class and train for related two-class classifiers. In the testing phase, to predict test samples’ class by probability estimates output of support vector machine. The experimental results show that the proposed method not only significantly reduces features and lower the complexity of classifiers, but also improves the localization accuracy.

Key words: indoor passive localization, feature selection, Markov blanket, support vector machine