摘要
运用小波理论，利用噪声与真实信号小波变换极大模性态之间的显著差异，提出了一类新的化学谱图数据自适应滤波算法，从根本上突破了现有算法均依据信噪频率特性进行滤波的传统模式．经大量色谱谱图数据处理试验证明，这种算法具有无需设置初始参数，消除人为误差因素对分析计算结果的影响，信噪分离性能好及峰位和峰高保持不变等一系列优点，其鲁棒性。
In this paper, a new type of adaptive filtering algorithm, which can adaptively
remove all kinds of noises from signals of analytical instruments under a variety of complex
conditions, is proposed. At present, the popular filtering algorithms which are widely applied to
the data processing equipment for analytical instrument are lowpass filter or bandpass filter.
The fundamental of those filters depends on the fact that the frequency characteristics of real
signals are different from those of noises. These filtering algorithms based on the different
frequency distribution characteristics between signals and noises have an obvious defect, that
is, users have to preset properly initial filter factors according to the width of peaks, which
greatly influences the objectivity and veracity of computational results in analytical procedures.
In the light of the wavelet transform modulus maximum theory proposed by Mallat, the
characteristics of wavelet transform modulus maxima of real signals are distinctively different
from those of noises in the practical signals of analytical instruments, such as chromatography.
It is easy to identify them. Taking advantage of the different characteristics between real
signals and noises on different scales in wavelet transformation domain, noises can be
removed from the practical signals of analytical instruments while avoiding to distort the real
signals. The adaptive filtering algorithm designed by this principle breaches the popular
patterns of current filtering algorithms, and radically improves the filtering effects. A lot of tests
using chromatography data prove that this algorithm has a serial of virtues, such as no
requirement on artificially presetting filter factors, excellent separation of signals and noises,
holding the position and height of peaks, and so on. Its performance in the robustness,
adaptability and fidelity of peak completely satisfy the needs of signal processing for analytical
instruments.
出处
《高等学校化学学报》
SCIE
EI
CAS
CSCD
北大核心
1999年第3期378-382,共5页
Chemical Journal of Chinese Universities
基金
国家自然科学基金
关键词
仪器分析
小波理论
信号处理
自适应滤波
滤波
Instrument analysis, Wavelet theory, Analytical signal processing,
Adaptive filtering
