摘要
基于全球卫星导航系统(GNSS)的水田旋耕平地机田间试验,采集平地机在调平过程中的倾角信号,采用小波硬阈值法,获取低频信号,并实时估计倾角信号的噪声方差,作为卡尔曼滤波的修正信息,再将低频信号作为系统输入,运用卡尔曼滤波对信号进行二次修正。试验结果表明:小波硬阈值–卡尔曼融合算法的滤波效果优于单一的小波阈值法和卡尔曼滤波,倾角信号经融合算法处理后,信号的信噪比由21.704提高到39.116,均方根误差从0.035 1减小至0.012 6。倾角信号中的噪声成分明显减少,信号的精确度更高。
Based on the global satellite navigation system(GNSS), the angle signal of the grader during the leveling process was detected by using paddy field rotary tiller field test. The wavelet hard threshold method was used to obtain the low frequency signal, and real-timely estimate the noise variance of the angle signal as the corrective information of Kalman filter. And then the second correction on the signal was performed using Kalman filtering with the system input of the low-frequency signal. The experimental results show that the wavelet hard threshold-Kalman fusion algorithm has better filtering effect than the single wavelet threshold method and Kalman filtering, respectively. When the inclination signal is processed by the fusion algorithm, the signal-to-noise ratio of the signal is increased from 21.704 to 39.116, and the root mean square error reduced from 0.035 1 to 0.012 6.
作者
梁友斌
许建康
周俊
张颖华
何瑞银
LIANG Youbin;XU Jiankang;ZHOU Jun;ZHANG Yinghua;HE Ruiyin(College of Engineering,Nanjing Agricultural University,Nanjing,Jiangsu 210031,China;Lianyungang Shuangya Machinery Ltd,Lianyungang,Jiangsu 222000,China)
出处
《湖南农业大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第2期248-252,共5页
Journal of Hunan Agricultural University(Natural Sciences)
基金
江苏省科学技术厅苏北科技专项(SZ-LYG2017009)。
关键词
水田旋耕平地机
倾角信号
小波阈值法
卡尔曼滤波
融合去噪算法
paddy field rotary-leveling machine
inclination signal
wavelet threshold method
Kalman filter
de-noising fusion algorithm
作者简介
梁友斌(1995-),男,山东日照人,硕士研究生,主要从事智能农业装备研究和开发,1604639963@qq.com;通信作者:周俊,博士,教授,主要从事农业装备智能化技术和农业机器人研究,zhoujun@njau.edu.cn。
