Determination of GPS receiver differential biases by neural network parameter estimation method

F., X.; Maruyama, Takashi; Ma, Guanyi; Takeda, Tatsuoki

doi:10.1029/2004rs003072

Cited by 32 publications

(21 citation statements)

References 18 publications

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“…While several techniques exist for determining these biases, one must take care in their application in regions outside their initial design [Lanyi and Roth, 1988;Ma and Maruyama, 2003;Ma et al, 2005;Rideout and Coster, 2006;Arikan et al, 2008]. Recent studies have attempted to characterize variabilities in these biases estimated through single-station approaches using real data and simulations [Ciraolo et al, 2007;Mazzella, 2009;Zhang et al, 2009;Brunini and Azpilicueta, 2010;Zhang et al, 2010;Conte et al, 2011;Coster et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

The nature of GPS differential receiver bias variability: An examination in the polar cap region

2015

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While modern GPS receiver differential code bias estimation techniques have become highly refined, they still demonstrate unphysical behavior, namely, notable solar cycle variability. This study investigates the nature of these seasonal and solar cycle bias variabilities in the polar cap region using single‐station bias estimation methods. It is shown that the minimization of standard deviation bias estimation technique is linearly dependent on the user's choice of shell height, where the sensitivity of this dependence varies significantly from 1 total electron content unit (1 TECU = 1016 el m−2) per 4000 km in solar minimum winter to in excess of 1 TECU per 90 km during solar maximum summer. Using an ionosonde, we find appreciable shell height variability resulting in bias variabilities of up to 2 TECU. Comparing northward face Resolute Incoherent Scatter Radar (RISR‐N) measurements to a collocated GPS station, we find that RISR‐derived GPS receiver biases vary seasonally but not with solar cycle. RMS differences between bias estimation methods and observation between 2009 and 2013 were found to range from 2.7 TECU to 3.4 TECU, depending on method. To account for the erroneous solar cycle variability of standard bias estimation approaches, we linearly fit these biases to sunspot number, removing the trend. RMS errors after sunspot detrending these biases are reduced to 1.91 TECU. Also, these ISR‐derived and sunspot‐detrended biases are fit to ambient temperature, where a significant correlation is found. By using these temperature‐fitted biases we further reduce RMS errors to 1.66 TECU. These results can be taken as further evidence of temperature‐dependent dispersion in the GPS cabling and antenna hardware.

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Section: Introductionmentioning

confidence: 99%

The nature of GPS differential receiver bias variability: An examination in the polar cap region

2015

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“…Comparison of our results on the real observation data with those by other methods is rather difficult because observation datasets are not the same, i.e., conditions of the ionosphere, positions of the satellites and the ground receivers are all different and, moreover, detailed quantitative data are not published usually, but our result is presumed among best values obtained by other three dimensional CITs from the viewpoint of the reconstruction errors of the NmF2 data [13,14], and the results of the model data analysis show that the error is limited almost by the spatial grid size. NNRMT is applicable to wide range of problems other than the tomographic image reconstruction described in this paper [25][26][27][28][29][30]. In this method various excellent features of the neural network are utilized and even numerical formulation of rather complicated problem can be carried out comparatively easily.…”

Section: Resultsmentioning

confidence: 99%

Ionospheric Tomography by Neural Network Collocation Method

Takeda

2007

Plasma and Fusion Research

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We describe a neural network collocation method (NNCM) for tomographic image reconstruction with small amount of projection data, which has been successfully applied to the three-dimensional ionospheric tomography based on the dataset of signal delays from the GPS satellites. In NNCM the neural network is trained by minimizing an object function composed of squared residuals of the governing equations evaluated at the collocation points and some constraining conditions imposed usually by observation data. This method is applied not only to the computerized tomography but also to the analyses of various inverse problems such as the data assimilation, the parameter estimation, the time series prediction, and so on.

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“…Besides the SCORE method, many advanced dataprocessing technologies and methods, such as neural networks (Ma et al, 2005), Kalman filter (Sardon et al, 1994;Anghel et al, 2009;Carrano et al, 2009), SCORPION method that removes the contribution of the plasmasphere using some model (Mazzella et al, 2002(Mazzella et al, , 2007, have been used in the process to estimate the instrumental biases in the last decade. Nevertheless, no matter which methods are employed, the condition of the temporal and spatial variation of the ionosphere has to be considered during the process to estimate the GPS instrumental biases from raw GPS data, and the large horizontal gradient of the ionospheric TEC caused by the EIA certainly will affect the accuracy of the estimated bias.…”

Section: Results and Analysismentioning

confidence: 99%

Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes

Zhang

et al. 2010

Ann. Geophys.

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Abstract. With one bias estimation method, the latituderelated error distribution of instrumental biases estimated from the GPS observations in Chinese middle and low latitude region in 2004 is analyzed statistically. It is found that the error of GPS instrumental biases estimated under the assumption of a quiet ionosphere has an increasing tendency with the latitude decreasing. Besides the asymmetrical distribution of the plasmaspheric electron content, the obvious spatial gradient of the ionospheric total electron content (TEC) along the meridional line that related to the Equatorial Ionospheric Anomaly (EIA) is also considered to be responsible for this error increasing. The RMS of satellite instrumental biases estimated from mid-latitude GPS observations in 2004 is around 1 TECU (1 TECU = 10 16 /m 2 ), and the RMS of the receiver's is around 2 TECU. Nevertheless, the RMS of satellite instrumental biases estimated from GPS observations near the EIA region is around 2 TECU, and the RMS of the receiver's is around 3-4 TECU. The results demonstrate that the accuracy of the instrumental bias estimated using ionospheric condition is related to the receiver's latitude with which ionosphere behaves a little differently. For the study of ionospheric morphology using the TEC derived from GPS data, in particular for the study of the weak ionospheric disturbance during some special geo-related natural hazards, such as the earthquake and severe meteorological disasters, the difference in the TEC accuracy over different latitude regions should be paid much attention.

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Determination of GPS receiver differential biases by neural network parameter estimation method

Cited by 32 publications

References 18 publications

The nature of GPS differential receiver bias variability: An examination in the polar cap region

The nature of GPS differential receiver bias variability: An examination in the polar cap region

Ionospheric Tomography by Neural Network Collocation Method

Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes

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