Noise Reduction Method for the Ring LaserGyro Signal Based on Ceemdan and the Savitzky-Golay Algorithm

被引：2

作者：

Liang, Hao ^{[1
,2
]}

Zhao, Xingfa ^{[2
]}

Guo, Yu ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China

[2] Beijing Aerosp Times Laser Inertial Technol Co Lt, Beijing 100094, Peoples R China

来源：

FLUCTUATION AND NOISE LETTERS | 2022年 / 21卷 / 01期

基金：

中国国家自然科学基金;

关键词：

The ring laser gyro; noise reduction; empirical mode decomposition; filtering;

D O I：

10.1142/S0219477522500055

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The ring laser gyro signal contains complex noise components, affecting the system's measurement accuracy. It is an engineering problem worthy of study to find an effective method to reduce the noise in the sampling signal and improve the system's accuracy. In order to reduce various noises of the ring laser gyroscope and improve its measurement accuracy, a noise reduction method combining complete ensemble empirical mode decomposition with adaptive noise and the Savitzky-Golay algorithm is proposed. First, the measured samples are mode decomposed, and the concept of weighted-permutation entropy is introduced to distinguish the noisy modes. Then, the Savitzky-Golay algorithm is used to process the noisy modes, and finally, the signal is reconstructed. Simulated test signal and actual gyro signal are used to test, and compared with the EMD noise reduction method, each indicator is improved. The paper proposes a new noise reduction method for the laser gyro signal, and introduces weighted-permutation entropy to analyze the dividing point. The test data show the effectiveness of the method.

引用

页数：19

共 34 条

[1]

Al-sharhan S, 2001, 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, P1135, DOI 10.1109/FUZZ.2001.1008855

[2] EMG signal filtering based on Empirical Mode Decomposition [J].

Andrade, Adriano O. ;

Nasuto, Slawomir ;

Kyberd, Peter ;

Sweeney-Reed, Catherine M. ;

Van Kanijn, F. R. .

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2006, 1 (01) :44-55

[3] Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition [J].

Balocchi, R ;

Menicucci, D ;

Santarcangelo, E ;

Sebastiani, L ;

Gemignani, A ;

Ghelarducci, B ;

Varanini, M .

CHAOS SOLITONS & FRACTALS, 2004, 20 (01) :171-177

[4] Permutation entropy: A natural complexity measure for time series [J].

Bandt, C ;

Pompe, B .

PHYSICAL REVIEW LETTERS, 2002, 88 (17) :4

[5] ECG signal denoising and baseline wander correction based on the empirical mode decomposition [J].

Blanco-Velasco, Manuel ;

Weng, Binwei ;

Barner, Kenneth E. .

COMPUTERS IN BIOLOGY AND MEDICINE, 2008, 38 (01) :1-13

[6]

Chen Ansheng, 2012, Proceedings of the 2012 8th IEEE International Symposium on Instrumentation and Control Technology (ISICT 2012), P178, DOI 10.1109/ISICT.2012.6291615

[7]

Dai S., 2020, GUANGDIAN GONGCHENGO, V47, DOI [10.12086/oee.2020.190137, DOI 10.12086/OEE.2020.190137]

[8]

Deng Chunlin, 2010, Chinese Journal of Sensors and Actuators, V23, P1435, DOI 10.3969/j.issn.1004-1699.2010.10.015

[9] Analysis and modeling of inertial sensors using Allan variance [J].

EI-Sheimy, Naser ;

Hou, Haiying ;

Niu, Xiaoji .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2008, 57 (01) :140-149

[10] Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information [J].

Fadlallah, Bilal ;

Chen, Badong ;

Keil, Andreas ;

Principe, Jose .

PHYSICAL REVIEW E, 2013, 87 (02)

← 1 2 3 4 →