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兵工学报 ›› 2016, Vol. 37 ›› Issue (3): 489-495.doi: 10.3969/j.issn.1000-1093.2016.03.014

• 论文 • 上一篇    下一篇

基于小波滤波及相关分析的激光光幕破片测速信号数据处理

张斌1, 李佳潞1, 赵冬娥1, 刘吉1, 李沅1, 史晓军2   

  1. (1.中北大学 信息与通信工程学院, 山西 太原 030051; 2.晋西工业集团有限公司, 山西 太原 030027)
  • 收稿日期:2015-09-01 修回日期:2015-09-01 上线日期:2016-05-24
  • 通讯作者: 张斌 E-mail:zhangbinsmart@163.com
  • 作者简介:张斌(1985—)男博士研究生
  • 基金资助:
    教育部科学研究重点项目(211027); 山西省研究生优秀创新项目(20143080)

Signal Processing of Laser Screen Fragments Velocity Measurement Based on Wavelet Transform and Correlation Analysis

ZHANG Bin1, LI Jia-lu1, ZHAO Dong-e1, LIU Ji1, LI Yuan1, SHI Xiao-jun2   

  1. (1.School of Information and Communication Engineering, North University of China, Taiyuan 030051, Shanxi, China;2.Jinxi Industries Group Go. Ltd, Taiyuan 030027, Shanxi, China)
  • Received:2015-09-01 Revised:2015-09-01 Online:2016-05-24
  • Contact: ZHANG Bin E-mail:zhangbinsmart@163.com

摘要: 针对激光光幕战斗部破片测速中信号噪声起伏大和无法自动判读的问题,提出了基于小波分析和相关算法的激光光幕破片测速信号自动识别与处理方法。该方法基于离散小波变换的带通滤波性质和多分辨率分析,联合小波阈值去噪方法,对破片过靶信号进行小波滤波;结合波峰检测获取各破片过靶的特征点计时时刻,根据同一破片飞行穿越前后光幕所捕获信号的相关性对各破片波形的归属进行自动识别和数据处理。通过对12组战斗部静爆现场采集到的波形进行处理,研究结果表明:该算法能够很好地滤除激光光幕破片测速信号中的高低频噪声,破片特征点的拾取率为96.9%,破片波形的归属识别率为87.2%.

关键词: 兵器科学与技术, 激光光幕, 破片测速信号, 小波分析, 相关分析, 自动识别, 速度

Abstract: A signal processing method based on the automatic identification algorithm for wavelet transform and correlation analysis is proposed for the large fluctuation noise and the impossible automatic discrimination of the laser screen warhead fragment velocity measuring signals. The proposed method removes the noises from the original signals by using the multi-resolution analysis and band-pass filtering of discrete waveform transform and the wavelet threshold denoising method. The time of the fragment flying through the laser screen can be obtained by the peak detection. And the automatic waveform identification of the same fragment is realized according to the correlation analysis of the signals of the same fragment flying through two laser screens. Twelve groups of the collected waveforms are processed. The results show that the proposed algorithm can remove the noises perfectly, 96.9% of recognition rate of fragments can be achieved, and the automatic identification rate of the corresponding waveform of the same fragment is 87.2%.

Key words: ordnance science and technology, laser screen, velocity measuring signal, wavelet transform, correlation analysis, automatic identification, velocity

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