摘要
为了能有效减少光电探测系统中的噪声干扰,提高光电探测系统的识别率,提出了一种基于小波分析和卡尔曼滤波相融合的光电探测目标回波数据去噪算法。通过对光电探测器采集的目标回波信号进行多尺度小波变换、阈值函数去噪和小波逆变换重构目标回波信号,并将重构后的目标回波信号作为卡尔曼滤波器状态最优估计中的测量值输入,进行分解去噪和最优估计,得到无干扰环境下的回波信号。通过计算与分析,结果表明小波卡尔曼滤波融合去噪算法比小波去噪处理算法的信噪比(SNR)提高了36%,均方根误差(RMES)减少了33%,能有效减少光电探测系统中的噪声干扰,提高了光电探测系统的稳定性。
In order to effectively reduce the noise interference and improve the recognition rate of the photodetection system,this paper proposes a photodetection target echo data denoising algorithm based on wavelet analysis and Kalman filtering.The reconstructed projectile target echo signal is obtained by multi-scale wavelet transform,threshold function denoising and wavelet inverse transform on the signal.Which is acquired by the photodetection system.Decomposition denoising and estimation by using the reconstructed signal as a Kalman filter input,getting interference-free signals.Through calculation and analysis,the results show that the wavelet Kalman filter fusion denoising algorithm used in this paper improves the signal-to-noise ratio(SNR)of the algorithm by 36%and the root mean square error(MES)by 33%.It can effectively reduce the noise interference and improve the stability of the photoelectric detection system.
作者
汶少阳
朱莉娜
李忠林
Wen Shaoyang;Zhu Lina;Li Zhonglin(School of Electronic and Information Engineering,Xi'an Technological University,Xi'an 710021,China)
出处
《电子测量技术》
2019年第21期119-122,共4页
Electronic Measurement Technology
关键词
光电探测
噪声干扰
小波分析
卡尔曼滤波
photoelectric detection
noise interference
wavelet analysis
Kalman filtering
