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作 者:赵薇 卓智海[1] 张月霞[1] ZHAO Wei;ZHUO Zhihai;ZHANG Yuexia(School of Information and Communication Engineering,Beijing Information Science and Technology University,Beijing 100101,China)
机构地区:[1]北京信息科技大学信息与通信工程学院,北京100101
出 处:《兵工学报》2024年第4期1264-1272,共9页Acta Armamentarii
基 金:北京市自然科学基金轨道交通联合基金重点研究专题项目(L191004)。
摘 要:针对弱生理信号在采集过程中易被噪声淹没,传统小波去噪算法存在去噪效果差和信号提取失真的问题,根据小波系数的能量分布特点,提出一种改进的小波阈值去噪算法。通过计算各层小波系数的能量来确定阈值,避免阈值计算的不平衡性,同时提高自适应性和弱信号的保真度;采用一种改进的可调节的双参数阈值函数对小波系数进行处理,在小波系数压缩程度可控的同时可以自由调节阈值函数的变化趋势。实验结果表明:改进的小波阈值去噪算法相较于两种传统去噪算法(经验模态分解算法和滤波器算法)以及12种传统小波阈值和阈值函数组合算法,在信噪比、均方根百分比和均方根误差上都具有明显的优势,并且在实测生理信号中取得了最小的平均相对误差和最小的波动性。To deal with the problems that the weak physiological signals are easily drowned by noise and the poor denoising effect and signal extraction distortion exist in traditional wavelet denoising method,an improved wavelet threshold denoising method based on the energy distribution characteristics of wavelet coefficients is proposed.The threshold is determined by calculating the energy of wavelet coefficient in each layer,which avoids the imbalance of threshold calculation while improving the adaptivity and the fidelity of weak signals.An improved adjustable two-parameter threshold function is used to process the wavelet coefficients,which allows the changing trend of threshold function to be freely adjusted while the degree of compression of the wavelet coefficients is controlled.The experimental results show that the improved wavelet threshold denoising method has more obvious advantages in signal-to-noise ratio,root-mean-square percentage and root-mean-square error compared with two traditional denoising algorithms(the empirical modal decomposition algorithm and the filter algorithm)and 12 traditional algorithms for combining wavelet thresholding and thresholding functions,and achieves the smallest average relative error and the smallest volatility in the measured physiological signal.
关 键 词:小波变换 能量梯度阈值 改进阈值函数 生理信号 去噪
分 类 号:TN957.51[电子电信—信号与信息处理]
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