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An end-to-end vehicle classification pipeline using vibrometry data

机译：使用测振数据的端到端车辆分类管道

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摘要

This paper evaluates and expands upon the existing end-to-end process used for vibrometry target classification and identification. A fundamental challenge in vehicle classification using vibrometry signature data is the determination of robust signal features. The methodology used in this paper involves comparing the performance of features taken from automatic speech recognition, seismology, and structural analysis work. These features provide a means to reduce the dimensionality of the data for the possibility of improved separability. The performances of different groups of features are compared to determine the best feature set for vehicle classification. Standard performance metrics are implemented to provide a method of evaluation. The contribution of this paper is to (1) thoroughly explain the time domain and frequency domain features that have been recently applied to the vehicle classification using laser-vibrometry data domain, (2) build an end-to-end classification pipeline for Aided Target Recognition (ATR) with common and easily accessible tools, and (3) apply feature selection methods to the end-to-end pipeline. The end-to-end process used here provides a structured path for accomplishing vibrometry-based target identification. This paper will compare with two studies in the public domain. The techniques utilized in this paper were utilized to analyze a small in-house database of several different vehicles.
机译：本文评估并扩展了用于振动测量目标分类和识别的现有端到端过程。使用振动测量特征数据进行车辆分类的一个基本挑战是确定鲁棒的信号特征。本文使用的方法论涉及比较自动语音识别，地震学和结构分析工作中的功能的性能。这些功能提供了一种减少数据维数的方法，以提高可分离性。比较不同特征组的性能，以确定用于车辆分类的最佳特征集。实施标准绩效指标以提供一种评估方法。本文的贡献在于（1）彻底解释最近已使用激光测振数据域应用于车辆分类的时域和频域特征，（2）建立用于辅助目标的端到端分类管道使用常见且易于访问的工具进行识别（ATR），并且（3）将特征选择方法应用于端到端管道。这里使用的端到端过程为完成基于振动测量的目标识别提供了结构化路径。本文将与公共领域的两项研究进行比较。本文中使用的技术用于分析几种不同车辆的小型内部数据库。

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