一种改进的BP-Adaboost算法及在雷达多目标分类上的应用
发布时间：2019-08-13 19:58
【摘要】：基于BP-Adaboost的目标分类算法用于雷达目标分类具有良好的效果.随着训练样本以及测试样本数增加,经典"一对多(One vs.Rest,OvR)"BP-Adaboost算法所需用时也随之增加.提出一种改进的多分类BP-Adaboost算法应用在雷达多目标分类上,在提高分类准确率的同时,有效地解决经典算法在多分类上时间开销过大的问题.该方法采用二进制方法重新表示样本数据类别,使用Adaboost算法将多个BP神经网络弱分类器集成起来学习,通过修改经典算法中的损失函数连续调整训练样本分布和弱分类器的权重,最终形成一个强分类器.对雷达高分辨率距离像(High Resolution Range Profile,HRRP)数据集进行分类仿真结果表明,相比于单个BP神经网络基学习器,所提算法的分类准确率提高了5%~10%,相比于经典的"一对多"BP-Adaboost算法,该算法所需用时仅为传统算法的1/2~1/3.
[Abstract]:The target classification algorithm based on BP-Adaboost has good effect in radar target classification. With the increase of training samples and the number of test samples, the time required for the classical "one-to-many (One vs.Rest,OvR)" BP-Adaboost algorithm also increases. An improved multi-classification BP-Adaboost algorithm is proposed for radar multi-target classification, which not only improves the classification accuracy, but also effectively solves the problem that the classical algorithm has too much time overhead in multi-classification. In this method, the binary method is used to rerepresent the sample data category, and the Adaboost algorithm is used to integrate multiple BP neural network weak classifiers. By modifying the loss function in the classical algorithm, the training sample distribution and the weight of the weak classifiers are continuously adjusted, and finally a strong classifier is formed. The classification simulation results of radar high resolution range profile (High Resolution Range Profile,HRRP) data set show that compared with a single BP neural network based learner, the classification accuracy of the proposed algorithm is improved by 5% 鈮，
本文编号：2526330
[Abstract]:The target classification algorithm based on BP-Adaboost has good effect in radar target classification. With the increase of training samples and the number of test samples, the time required for the classical "one-to-many (One vs.Rest,OvR)" BP-Adaboost algorithm also increases. An improved multi-classification BP-Adaboost algorithm is proposed for radar multi-target classification, which not only improves the classification accuracy, but also effectively solves the problem that the classical algorithm has too much time overhead in multi-classification. In this method, the binary method is used to rerepresent the sample data category, and the Adaboost algorithm is used to integrate multiple BP neural network weak classifiers. By modifying the loss function in the classical algorithm, the training sample distribution and the weight of the weak classifiers are continuously adjusted, and finally a strong classifier is formed. The classification simulation results of radar high resolution range profile (High Resolution Range Profile,HRRP) data set show that compared with a single BP neural network based learner, the classification accuracy of the proposed algorithm is improved by 5% 鈮，
本文编号：2526330
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