Predicting Long-Term Stability of Precise Oscillators under Influence of Frequency Drift

High-performance oscillators, atomic clocks for instance, are important in modern industries, finance and scientific research. In this paper, the authors study the estimation and prediction of long-term stability based on convex optimization techniques and compressive sensing. To take frequency drift into account, its influence on Allan and modified Allan variances is formulated. Meanwhile, expressions for the expectation and variance of discrete-time Hadamard variance are derived. Methods that reduce the computational complexity of these expressions are also introduced. Tests against GPS precise clock data show that the method can correctly predict one-week frequency stability from 14-day measured data

Sequential Ambiguity Resolution Method for Poorly-Observed GNSS Data

Integer ambiguity resolution is required to obtain precise coordinates for the global navigation satellite system (GNSS). Poorly observed data cause unfixed integer ambiguity and reduce the coordinate accuracy. Previous studies mostly used denoise filters and partial ambiguity resolution algorithms to address this problem. This study proposes a sequential ambiguity resolution method that includes a float solution substitution process and a double-difference (DD) iterative correction equation process. The float solution substitution process updates the initial float solution, while the DD iterative correction equation process is used to eliminate the residual biases. The satellite-selection experiment shows that the float solution substitution process is adequate to obtain a more accurate float solution. The iteration-correction experiment shows that the double-difference iterative correction equation process is feasible with an improvement in the ambiguity success rate from 28.4% to 96.2%. The superiority experiment shows significant improvement in the ambiguity success rate from 36.1% to 83.6% and a better baseline difference from about 0.1 m to 0.04 m. It is proved that the proposed sequential ambiguity resolution method can significantly optimize the results for poorly-observed GNSS data

Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading

The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under the framework of World Geodetic System 1984 (WGS84) from 2000 to 2021. Different noise model combinations are used for noise analysis, and the optimal noise model for each station before and after hydrologic loading correction is calculated. The results show that the noise models of global IGS reference stations are diverse, and each component has different optimal noise model characteristics, mainly white noise + flicker noise (WN+FN), generalized Gauss–Markov noise (GGM) and white noise + power law noise (WN+PL). Through specific analysis between the optimal noise model and the time series velocity of the station, it is found that the maximum influence value of the vertical velocity can reach 1.8 mm when hydrological loading is considered. Different complex noise models also have a certain influence on the linear velocity and velocity uncertainty of the station. Among them, the influence of white noise + random walking noise is relatively obvious, and its maximum influence value in the elevation direction can reach over 2 mm/year. When studying the impact of hydrological loading correction on the periodicity of the coordinate series, it is concluded whether the hydrological loading is calculated or not, and the GNSS long-term coordinate series has obvious annual and semi-annual amplitude changes, which are most obvious in the vertical direction, up to 16.48 mm

Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32

The baseband signal of the 406 MHz COSPAS-SARSAT emergency beacon is a 144-bit or 112-bit data with 0 and 1 code, including the information bits and error correcting check bits. In order to improve the coding efficiency of the baseband signal for the 406 MHz transmitter in the STM32 micro-controller, this paper proposes a calculation method for the first BCH error correcting code (BCH-1) and the second BCH error correcting code (BCH-2) of the COSPAS-SARAST transmitter. First, conduct the data coding process by 64-bit integral data; then, compute the BCH (127, 106) code and the BCH (63, 51) code to obtain the respective error correcting codes for improving coding rate; next, generate the baseband signal according to the GB 14391-2021 coding format; finally, modulate the baseband signal into a 406 MHz-RF signal, send it out and complete the task of distress alerting. The experimental results show that the proposed STM32 microcontroller-based baseband signal generation method is effective, and the EPIRB (Emergency Position Indicating Radio Beacon) designed based on this method has reached the industry standard

2D Semantic Segmentation: Recent Developments and Future Directions

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined

Low-power, high-linearity transconductor with a high tolerance for process and temperature variations

Funding Information: This research was supported by the Major State Basic Research Development Programme of China (2018YFE0206500). Publisher Copyright: © The Institution of Engineering and Technology 2020. This is the peer reviewed version of the following article: Zhao, J., Sun, Y., Nie, G., Simpson, O. and Xu, W. (2020), Low‐power, high‐linearity transconductor with a high tolerance for process and temperature variations. IET Circuits Devices Syst., 14: 1295-1304. https://doi.org/10.1049/iet-cds.2019.0565. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.A novel scheme for tunable complementary metal–oxide–semiconductor (CMOS) transconductor robust against process and temperature (PT) variations is presented. The proposed configuration is a voltage controlled circuit based on a double negative channel-metal-oxide-semiconductor (NMOS) transistor differential pairs connected in parallel, which has low power and high linearity. The PT compensation is completed by two identical PT compensation bias voltage generators (PTCBVGs), which can guarantee the designed transconductor high tolerance for PT variations. A complete CMOS transconductor with PTCBVG has been designed and simulated using 0.18 μm technology. The effectiveness of PT compensation technique is proved. The simulation results of post-layout are commensurate with pre-layout. Post-layout simulation results show that when temperature changes from - 40 to 85°C for different process corners (TT, SS, SF, FS and FF), the transconductance varies from 91.8 to 123.6 μS, the temperature coefficient is <1090 ppm/°C, the total harmonic distortion is from - 78 to -72dB at 1 MHz for 0.2 V PP input signal, -3 dB bandwidth changes from 2.5 to 5 GHz, input-referred noise varies from 78.1 to 124.8 nV/sqartHz at 1 MHz and DC power is from 1.5 to 3.2 mW.Peer reviewe

Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System

The BeiDou navigation satellite system (BDS-3) has been deployed and provides positioning, navigation, and timing (PNT) services for users all over the world. On the basis of BDS-2 system signals, BDS-3 adds B1C, B2a, B2b, and other signals to realize compatibility and interoperability with other global navigation satellite systems (GNSS). Network real-time kinematic (RTK) technology is an important real-time regional high-precision GNSS positioning technology. Combined with the network RTK high-precision service platform software developed by the author’s research group and the measured data of a provincial continuously operating reference station (CORS) in Hubei, this paper preliminarily evaluates the network RTK service performance under the new signal system of BDS-3. The results show that single BDS-3 adopts the new signal combination (B1C+B2a) and transition signal combination (B1I+B3I) when providing virtual reference station (VRS) services, the RTK fixation rate of the terminal is above 95%, and the horizontal and elevation accuracies are within 1cm and 2 cm, respectively, which meets the requirements of providing high-precision network RTK services by a single BDS-3 system. In addition, the positioning accuracy of BDS-2 is relatively poor, while the accuracy of BDS-3 is better than global positioning systems (GPS) and BDS-2. The combined processing effect of the B1I+B3I transition signal of BDS-2/3 is optimal, the accuracy of E and N directions is better than 0.5 cm, and the accuracy of U direction is better than 1.5 cm. It can be seen from the atmosphere correction accuracy, regional error modeling accuracy, and network RTK terminal positioning accuracy that the service effect of the B1C+B2a combination is slightly better than that of the B1I+B3I combination. When a single BDS-3 constellation provides network RTK services, it is recommended to use the B1C+B2a combination as the main frequency solution, and when the BDS-2/3 constellation provides service, it is recommended to use the B1I+B3I combination as the main frequency solution

Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction