Purpose

The purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator.

Design/methodology/approach

Firstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise.

Findings

Through the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified.

Practical implications

The real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data.

Originality/value

Firstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.

中文翻译：

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移动平均算子的谱分析及时频混合序列算子的构建

目的

本文旨在分析移动平均算子的谱特性，提出一种新的时频混合序列算子。

设计/方法/方法

首先，通过傅里叶变换将复数数据转换为频域数据。构建适当的频域算子以消除干扰的影响。然后，逆傅立叶变换将去除了干扰的频域数据变换为时域数据。最后，根据谱特征选择合适的N项移动平均算子，消除周期因素和噪声的影响。

发现

通过对微波传感器实时传感记录的数据进行频谱分析，可以明确数据中的频谱特征和信息、噪声和冲击干扰因素的范围。

实际影响

一次药物成分监测的实时数据分析结果表明，混合序列算子对抑制数据中隐含的扰动、周期因素和噪声有很好的效果。

原创性/价值

首先，本文提出了移动平均算子和新型时频混合序列算子的谱分析。对于复杂的数据，单独应用频域算子或时域算子很难达到理想的效果。时频混合序列算子可以得到比较满意的结果。