| 伍弘毅,陈志聪,吴丽君,何虔恩.利用加速度信号时频域特征的枪击识别研究[J].电子测量与仪器学报,2022,36(5):180-187 |
| 利用加速度信号时频域特征的枪击识别研究 |
| Research on recognition of gun shooting using accelerationsignal’s features in both time and frequency domain |
| |
| DOI： |
| 中文关键词: 射击检测 加速度传感器 机器学习 特征提取 |
| 英文关键词:gun shooting detection acceleration sensor machine learning feature extraction |
| 基金项目:国家自然科学基金（61601127）、福建省工业和信息化厅（82318075）、福建省自然科学基金面上项目（2021J01580）资助 |
|
|
|
|
| 摘要点击次数: 225 |
| 全文下载次数: 813 |
| 中文摘要: |
| 当前枪支射弹可靠检测及精确计数是枪弹管控的难点之一。 为提高基于加速度信号的射弹检测算法的精度和可靠性,
提出一种新的射击信号时域特征提取方法—时域分段特征提取法,可避免时域特征过度依赖于加速度瞬时尖峰的问题。 首先,
提取了枪击加速度样本信号的时域和频域各类统计特征。 然后,采用机器学习分类算法 K 近邻、逻辑回归、支持向量机以及决
策树和随机森林进行枪击识别建模。 最后,探索和比较各种单一特征对枪击事件识别模型性能的影响。 实验结果表明,所提取
的主波动域面积特征具有最优的区分度,能够在多数机器学习算法上达到 99%以上的分类准确率。 |
| 英文摘要: |
| Currently, reliable detection and accurate counting of firearm projectiles is one of the difficult points of gun and ammunition
management. To improve the accuracy and reliability of projectile detection algorithm based on acceleration signals, we propose a new
time-domain feature extraction method for firearm firing signals: The time-domain segmental feature extraction method, which avoids the
problem that time-domain features are overly dependent on acceleration transient spikes. Firstly, various statistical features of the sample
signals of gunshot acceleration in the time and frequency domains have been extracted. Then machine learning classification algorithms
K-nearest neighbors, logistic regression, support vector machines, decision trees and random forests are used for gunshot recognition
modeling. Finally, the effects of various single features on the performance of gunshot recognition models are explored and compared.
The experimental results show that the extracted main fluctuation domain area feature have the optimal discrimination and can achieve
more than 99% classification accuracy on most machine learning algorithms. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
