小波阈值去噪在侧扫雷达监测数据去噪中的应用

Application of Wavelet Threshold Denoising in the Monitoring Data Denoising of Side Sweep Radar

侧扫雷达在线监测系统采集的单元表面流速实时数据不可避免的受到各种噪声的干扰污染,导致基于原始单元表面流速实时数据的流量模型存在偏差,阻碍了真正实现流量实时监测和提高流量在线自动监测的精度。该文对小波阈值去噪方法应用于侧扫雷达监测数据去噪做了详尽的探讨研究,并利用五点均滑法和小波阈值去噪方法对侧扫雷达监测数据进行了去噪对比实验,结果表明:小波阈值去噪应用于侧扫雷达监测数据的去噪效果比五点均滑法好;小波阈值去噪效果较好,峰值损失较小,且涨落消除彻底,去噪后数据既能较好的保留了原始数据特性,又达到很好的去噪效果,为侧扫雷达流量模型建立提供了优质数据支撑,为侧扫雷达在线监测系统真正实现流量实时监测和提高流量监测精度奠定了基础。

The real-time data of unit surface flow velocity collected by the online monitoring system of side-scan radar are inevitably contaminated by various noises,which leads to the deviation of the flow model based on the real-time data of the original unit surface flow velocity,and hinders the realization of real-time flow monitoring and the improvement of the accuracy of online automatic flow monitoring.The application of wavelet threshold denoising method to the denoising of side-scan radar monitoring data is discussed in detail,and the comparison experiment of denoising of side-scan radar monitoring data is carried out by using the five-point average sliding method and the wavelet threshold denoising method.The results show that the denoising effect of wavelet threshold denoising applied to the side-scan radar monitoring data is better than that of the five-point average sliding method.The wavelet threshold denoising effect is good,the peak loss is small,and the fluctuation is eliminated completely.The denoised data can not only retain the characteristics of the original data,but also achieve good denoising effect.It provides high-quality data support for the establishment of the flow model of side-scan radar,and lays the foundation for the real-time flow monitoring and the improvement of the flow monitoring accuracy of the side-scan radar online monitoring system.