scholarly journals Singular spectrum analysis for precision improvement in dual-comb laser ranging

2018 ◽  
Vol 67 (1) ◽  
pp. 010601
Author(s):  
Cao Hui ◽  
Song You-Jian ◽  
Yu Jia-He ◽  
Shi Hao-Sen ◽  
Hu Ming-Lie ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Laser Ranging ◽  
Singular Spectrum

scholarly journals Reduction of Moving Target Time-of-Flight Measurement Uncertainty in Femtosecond Laser Ranging by Singular Spectrum Analysis Based Filtering

2018 ◽  
Vol 8 (9) ◽  
pp. 1625 ◽  
Author(s):  
Hui Cao ◽  
Youjian Song ◽  
Yuepeng Li ◽  
Runmin Li ◽  
Haosen Shi ◽  
...  
Keyword(s):  
Femtosecond Laser ◽  
Spectrum Analysis ◽  
Femtosecond Lasers ◽  
Singular Spectrum Analysis ◽  
Laser Ranging ◽  
Moving Target ◽  
Moving Targets ◽  
Practical Applications ◽  
Singular Spectrum ◽  
Free Running

Femtosecond laser ranging has drawn great interest in recent years, particularly based on an asynchronous optical sampling implementation where a pair of femtosecond lasers are used. High precision absolute ranging either relies on tightly-phase-locked optical frequency combs (a dual-comb setup) or multiple averaging of the measurements from two free-running femtosecond lasers. The former technique is too complicated for practical applications, while the latter technique does not apply to moving targets. In this report, we propose a new route to utilizing a powerful singular spectrum analysis (SSA) filtering method to improve femtosecond laser ranging precision for moving targets with acceleration. The SSA method is capable of separating complex patterns in signals without a priori knowledge of the dynamical model. Here, we utilize the basic SSA filter to extract the target trajectory in the presence of measurement noise both in numerical simulation and in the absolute ranging experiment based on a pair of free-running femtosecond lasers. The experimentally-achieved absolute ranging uncertainty of a moving target is well below 110 nm at a 200-Hz update rate by applying the basic SSA filter. This method paves the way to the practical applications of femtosecond absolute ranging for dynamic objects.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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