Multi-category classification of ground vehicles based on the acoustic data of multiple terrains using fuzzy logic rule-based classifiers
Multi-category classification of ground vehicles based on the acoustic data of multiple terrains using fuzzy logic rule-based classifiers
复制标题
使用基于模糊逻辑规则的分类器，基于多个地形的声学数据对地面车辆进行多类别分类
DOI：
10.1117/12.603144
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
J. Mendel
中科院分区：
文献类型：
--
作者：
Hongwei Wu;J. Mendel
地面车辆的声发射包含了丰富的信息，这些信息可以用于车辆分类，例如在战场上。然而，从声学测量中提取的特征是时变的，并且包含很多不确定性，特别是当从多个地形获得声学测量时，这使得分类具有挑战性。在本文中，我们提出了我们的研究的多类别分类的地面车辆的基础上的声学数据的四个环境条件。我们的目标是设计一个分类器，可以在所有四个地形没有一个特定的地形先验知识。我们首先进行数据预处理（包括消除冗余记录，处理数据失真和生成原型），特征提取和不确定性分析。然后，我们开发的贝叶斯分类器，1型（T1）和区间型2（T2）模糊逻辑规则为基础的分类器（FLRBC）。这些分类器具有类似的架构，由四个子系统组成，每个子系统用于一个地形，并且对于每个地形上的每种车辆具有一个概率模型（贝叶斯分类器）或一个模糊逻辑规则（T1和间隔T2 FLRBC）。它们的不同之处在于实现这种通用架构的方式。我们还提出了实验的结果，以评估所有分类器的性能。实验结果表明：（1）T1和间隔T2 FLRBC的分类性能均优于贝叶斯分类器，且间隔T2 FLRBC的分类性能优于T1 FLRBC：（2）采用基于多数表决的时间决策融合时，各分类器的分类错误率平均值较小，但标准差略大;（3）当采用基于多数表决的时间决策融合时，T1和T2间隔FLRBC的性能均优于贝叶斯分类器，且T2间隔FLRBC的性能优于T1 FLRBC。
The acoustic emissions of a ground vehicle contain a wealth of information, which can be used for vehicle classification, e.g. in the battlefield. However, features that are extracted from the acoustic measurements are time-varying and contain a lot of uncertainties, especially when the acoustic measurements are obtained from multiple terrains, which makes the classification challenging. In this paper we present our study on the multi-category classification of ground vehicles based on the acoustic data of four environmental conditions. The goal is to design one classifier that can operate in all four terrains without a priori knowledge of a specific terrain. We first perform the data pre-processing (including elimination of redundant records, processing of data distortion and generation of prototypes), feature extraction, and uncertainty analysis. We then develop the Bayesian classifier, and type-1 (T1) and interval type-2 (T2) fuzzy logic rule-based classifiers (FLRBC). These classifiers have similar architectures, consist of four sub-systems each for one terrain, and have one probability model (Bayesian classifier) or one fuzzy logic rule (T1 and interval T2 FLRBCs) for each kind of vehicle on each terrain. They differ in the way that this common architecture is implemented. We also present the results of the experiments to evaluate the performance of all classifiers. Experimental results reveal that (1) both the T1 and interval T2 FLRBCs have better performance than the Bayesian classifier, and the interval T2 FLRBC has better performance than the T1 FLRBC; (2) each classifier has a smaller average but a slightly larger standard deviation of classification error rates when the majority voting-based temporal decision fusion is applied; and (3) when the majority voting-based temporal decision fusion is applied, both the T1 and interval T2 FLRBCs have better performance than the Bayesian classifier, and the interval T2 FLRBC has better performance than the T1 FLRBC.