Developing an Innovative High-precision Approach to Predict Medium-term and Long-term Satellite Clock Bias

A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias (SCB). Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server (IGS) server. The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models, with the training time being much shorter than the wavelet neural network model

Robust UKF algorithm with motion constraint in BDS navigation

In a complex urban environment,navigation based on single BeiDou navigation satellite system (BDS) will be interfered by multipath (MP) and non-line-of-sight (NLOS) signals, which degrades the accuracy of positioning. In order to solve this problem, a robust unscented Kalman filter (UKF) with motion constraint is proposed. The algorithm constructs an equivalent weight function based on the innovation vector, which will overcome the problem of performance degradation of conventional robust method caused by inaccurate initial values of the position and receiver clock offset. In addition, the approximate moving direction and elevation of the carrier are used to further enhance the filtering solution. The actual on-board experiment results show that this method can effectively suppress the interference of MP and NLOS signals, and improve the accuracy of BDS navigation in urban environments

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Abstract Multisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting

GPS Partial Ambiguity Resolution Method for Zero-difference Precise Point Positioning

With the development of ambiguity resolution for precise point positioning(PPP) in recent years, it can improve positioning accuracy of PPP with short time observations since ambiguities can be fixed to right integers. However, unacceptable errors would be introduced into coordinate parameters if ambiguities are fixed to wrong integers. It's necessary to investigate the reliability and success rate of ambiguity-fixed PPP. This paper investigates PPP ambiguity fixing method based on zero-differenced fractional-cycle biases(FCBs). A partial ambiguity resolution(PAR) strategy based on cascaded quality control is proposed. Data sets from Europe CORS are used to validate and demonstrate PAR strategy. Results have showed that PPP ambiguity fixing can improve positioning accuracy of hourly PPP solution. When the strategy of PAR is applied, the influence of un-convergence ambiguities can be controlled efficiently and the success rate of PPP ambiguity fixing is improved

Spontaneous single nucleotide polymorphism in porcine microRNA-378 seed region leads to functional alteration

<p>Sequence variation in a microRNA (miRNA) seed region can influence its biogenesis and effects on target mRNAs; however, in mammals, few seed region mutations leading to functional alterations have been reported to date. Here, we report the identification of a single nucleotide polymorphism (SNP) with functional consequence located in the seed region of porcine miR-378. <i>In vitro</i> analysis of this rs331295049 A17G SNP showed significantly up-regulated expression of the mature miR-378 (miR-378/G). <i>In silico</i> target prediction indicated that the SNP would modulate secondary structure and result in functional loss affecting >85% of the known target genes of the wild-type miR-378 (miR-378/A), and functional gain affecting >700 new target genes, and dual-luciferase reporter assay verified this result. This report of a SNP in the seed region of miR-378 leads to functional alteration and indicates the potential for substantive functional consequences to the molecular physiology of a mammalian organism.</p> <p>The SNP changed the secondary structure of pre-miR-378 and increased the production of mutant miR-378. Functionally, the SNP lead to loss and gain of miR-378 targets.</p