Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Kłos, Anna; Bos, M. S.; Fernandes, Rui; Bogusz, Janusz

doi:10.1007/s11004-018-9760-z

Cited by 26 publications

(10 citation statements)

References 54 publications

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“…The equatorial bulge in white noise amplitudes remains visible, with a minimum of 2.0 mm near the poles and a maximum of about 4.0 mm at the equator. This bulge was originally pointed out by Williams et al (2004) but was also more recently observed in IGS station position time series by Klos et al (2019). Williams et al (2004) suggested that the origin of this bulge could reside in mis-modeling of the atmospheric delays affecting GNSS observations.…”

Section: Spatial Variability Of Variance Componentsmentioning

confidence: 63%

Influence of Aperiodic Non‐Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series

et al. 2021

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Monitoring vertical land motions (VLMs) at the level of 0.1 mm/yr remains one of the most challenging scientific applications of global navigation satellite systems (GNSS). Such small rates of change can result from climatic and tectonic phenomena, and their detection is important to many solid Earth‐related studies, including the prediction of coastal sea‐level change and the understanding of intraplate deformation. Reaching a level of precision allowing to detect such small signals requires a thorough understanding of the stochastic variability in GNSS VLM time series. This paper investigates how the aperiodic part of non‐tidal atmospheric and oceanic loading (NTAOL) deformations influences the stochastic properties of VLM time series. Using the time series of over 10,000 stations, we describe the impact of correcting for NTAOL deformation on 5 complementary metrics, namely: the repeatability of position residuals, the power‐spectrum of position residuals, the estimated time‐correlation properties, the corresponding velocity uncertainties, and the spatial correlation of the residuals. We show that NTAOL deformations cause a latitude‐dependent bias in white noise plus power‐law model parameter estimates. This bias is significantly mitigated when correcting for NTAOL deformation, which reduces velocity uncertainties at high latitudes by 70%. Therefore, removing NTAOL deformation before the statistical analysis of VLM time series might help to detect subtle VLM signals in these areas. Our spatial correlation analysis also reveals a seasonality in the spatial correlation of the residuals, which is reduced after removing NTAOL deformation, confirming that NTAOL is a clear source of common‐mode errors in GNSS VLM time series.

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Section: Spatial Variability Of Variance Componentsmentioning

confidence: 63%

Influence of Aperiodic Non‐Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series

et al. 2021

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“…A clean audio file fetched from the audio datastore is corrupted with a noisy segment extracted from the noise signal. These signals are plotted in the fig 5. Then these signals are passed to the network model and then model is trained.…”

Section: Fig 5 Fully Connected Networkmentioning

confidence: 99%

A Study on Effectiveness of Deep Neural Networks for Speech Signal Enhancement in Comparison with Wiener Filtering Technique

Padarti¹,

Polavarapu²,

Madiraju³

et al. 2021

Preprint

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“…Wiener filter 20,21 is a linear time invariant system, which is usually used to extract useful signals contaminated by noise. The filter is realized by the minimum mean square error criterion, and the filter coefficient can be adjusted adaptively according to the input signal.…”

Section: Kalman Filter Innovation Algorithmmentioning

confidence: 99%

Kalman filter tracking of sequence spot centroid ablated by femtosecond laser

Wang

Liu

Wang

2020

Micro & Optical Tech Letters

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In the process of processing monocrystalline silicon by femtosecond laser, there is a phenomenon of derived plasma luminescence, which formed the time series spot. The laser spot image contains some effective information related to process parameters, such as ablation power of femtosecond laser, processing speed and so on. Through the comprehensive analysis of the geometric features of the sequential spot image, it can be used in the closed‐loop feedback system of micromachining based on the features of the plasma spot image. Because the spot centroid can be used to locate the spot position accurately, the sequential spot centroid obtained by gray centroid method was used as the sampling data, which was tracked by Kalman filter, and then the movement law of the spot was observed and analyzed. In this paper, the discrete linear Kalman filter algorithm is used to establish motion model of the spot centroid, and the next movement state of spot is predicted, and then the trajectory of the spot is obtained. Generally, the traditional Kalman filter has some shortcomings, such as inaccurate model, too much noise, and it is difficult to achieve the optimal estimation, therefore, the application of Kalman filter in spot centroid tracking is optimized. Firstly, the process noise is updated in real time to correct the direction of acceleration accurately and reduce the uncontrollability of process noise by introducing the factor l; Secondly, combined with Wiener filter, the observation value is optimized to minimize the influence of observation noise; Thirdly, starting from the Kalman filter equation, the actual value of the previous moment is connected with the optimal estimated value by weighted addition based on the “Identity of A‐OE” operator, so as to improve the prediction accuracy of the current time. Finally, the EF‐BP neural network is introduced on the basis of the “Identity of A‐OE” operator, and a stable training system is used to optimize the weight repeatedly, so that the error can reach the expected value. The results show that the error of the observation value is reduced by 8.2503%, the error of the optimal filter estimation value is reduced to 0.3490, and the error reduction range is 6.7662. The centroid tracking accuracy of the ablated spot image is improved obviously.

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Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Cited by 26 publications

References 54 publications

Influence of Aperiodic Non‐Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series

Influence of Aperiodic Non‐Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series

A Study on Effectiveness of Deep Neural Networks for Speech Signal Enhancement in Comparison with Wiener Filtering Technique

Kalman filter tracking of sequence spot centroid ablated by femtosecond laser

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