Global Navigation Satellite System Navigation Research Articles

3D Galileo Reference Antenna Pattern for Space Service Volume Applications.

There is an increasing demand for navigation capability for space vehicles. The exploitation of the so-called Space Service Volume (SSV), and hence the extension of the Global Navigation Satellite System (GNSS) from terrestrial to space users, is currently considered a fundamental step. Knowledge of the constellation antenna pattern, including the side lobe signals, is the main input for assessing the expected GNSS signal availability and navigation performance, especially for high orbits. The best way to define and share this information with the final GNSS user is still an open question. This paper proposes a novel methodology for the definition of a high-fidelity and easy-to-use statistical model to represent GNSS constellation antenna patterns. The reconstruction procedure, based on antenna characterization techniques and statistical learning, is presented here through its successful implementation for the "Galileo Reference Antenna Pattern (GRAP)" model, which has been proposed as the reference model for the Galileo programme. The GRAP represents the expected Equivalent Isotropic Radiated Power (EIRP) variation for the Galileo FOC satellites, and it is obtained by processing the measurements retrieved during the characterization campaign performed on the Galileo FOC antennas. The mathematical background of the model is analyzed in depth in order to better assess the GRAP with respect to different objectives such as improved resolution, smoothness and proper representation of the antenna pattern statistical distribution. The analysis confirms the enhanced GRAP properties and envisages the possibility of extending the approach to other GNSSs. The discussion is complemented by a preliminary use case characterization of the Galileo performance in SSV. The accessibility, a novel indicator, is defined in order to represent in a quick and compact manner, the expected Galileo SSV quality for different altitudes and target mission requirements. The SSV characterization is performed to demonstrate how simply and effectively the GRAP model can be inserted into user analysis. The work creates the basis for an improved capability for assessing Galileo-based navigation in SSV according to the current knowledge of the antenna pattern.

İnsansız Hava Araçları (İHA) İçin Küresel Navigasyon Uydu Sistemi (GNSS) Bağımsız Navigasyon

The ability of Unmanned Aerial Vehicles (UAV) to perform autonomous navigation depends on the accurate determination of their positions provided by the Global Navigation Satellite System (GNSS). For position determination and environmental orientation during flight, UAVs are usually equipped with electronic equipment such as GNSS, Inertial Measurement Unit (IMU), gyroscope and accelerometer. However, the GNSS signal may be lost or distorted due to poor weather, obstacles or terrain, the unfavorable position of satellites, spoofing and jamming. In such cases of GNSS signal loss or deterioration, the IMU alone becomes unable to provide reliable UAV location information. Especially in cases where there is not enough visibility and the UAV cannot be brought to the take-off point by manual operation, the loss of the GNSS signal causes great losses. In this paper, GNSS independent flight and navigation studies are included. It is seen that the use of hybrid navigation solutions has great importance in GNSS independent UAV flights.

Real-time retrieval of high-precision ZTD maps using GNSS observation

Zenith Tropospheric Delay (ZTD) is an important factor that restricts the high-precision positioning of global navigation satellite system (GNSS), and it is of great significance in establishing a real-time and high-precision ZTD model. However, existing ZTD models only consider the impact of linear terms on ZTD estimation, whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study. A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the real-time linear and nonlinearity ZTD (RLNZ) model. This model uses the ZTD estimated from the Global Pressure and Temperature 3 (GPT3) model as the initial value. The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model is first considered. In addition, nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method. Finally, the RLNZ-derived ZTD is obtained at an arbitrary location. The western United States, with height difference ranging from −500 to 4000 m, is selected, and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory (NGL) and the data of 9 radiosonde (RS) stations in the year 2021 are used. Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model. Statistical results show the averaged root mean square (RMS), Bias, and mean absolute error (MAE) of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm, respectively. The average improvement rate of the RLNZ model is 33 % when compared to the GPT3 model. Finally, the application of the proposed RLNZ model in simulated real-time Precise Point Positioning (PPP) indicates that the accuracy of PPP in N, E and U components is improved by 8 %, 2 %, and 6 % when compared with that from the GPT3-based PPP. Meanwhile, the convergence time in N and U components is improved by 23 % and 7 %, respectively. Such results verify the superiority of the proposed RLNZ model in retrieving real-time ZTD maps for GNSS positioning and navigation applications.

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).

A multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles

Due to the difficulty of Global Navigation Satellite System (GNSS) in urban canyon environments, GNSS and inertial navigation system (INS) fusion positioning is widely used in the field of intelligent driving. In actual positioning, INS usually uses the vehicle physical model as a constraint to calculate inertial measurement unit (IMU) data to supplement the vehicle position and attitude as GNSS positioning information. However, the existing algorithms usually use a fixed physical model. When the vehicle’s driving behavior changes, such as lane-changing, acceleration, deceleration, etc., the fixed model cannot provide long-term constraints on cumulative errors, resulting in a decrease in fusion positioning accuracy and stability. Therefore, this paper proposes a multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles. First, to adapt to multimodal driving behavior, a multimodal assisted positioning physical model with adaptive driving behavior (MAPM) is established, which expresses different physical models probabilistically based on driving behavior and historical data. Secondly, high-precision estimation of vehicle positioning parameters is achieved based on model probability and interactive multi-model (IMM). Finally, effective fusion of MAPM/GNSS/INS positioning information is achieved under the federated Kalman filtering framework. The simulation results show that compared to traditional GNSS/INS fusion positioning, the proposed method can effectively improve positioning accuracy and stability, especially in driving conditions with changes in driving behavior.

LIDAR SLAM-AIDED VEHICULAR NAVIGATION SYSTEM FOR GNSS-DENIED ENVIRONMENTS

Abstract. Typically, in situations where Global Navigation Satellite System (GNSS) signals are unavailable, navigation systems rely on integrating GNSS and inertial navigation system (INS) data. While such integration can provide accurate positioning during short GNSS signal outages, it cannot sustain prolonged GNSS outages. The reason for this is that the system's performance depends solely on the INS and can result in significant errors over time. To address this issue, additional onboard sensors are necessary. This study proposes a navigation system that integrates INS and LiDAR simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF). The system was tested using the raw KITTI dataset in various outdoor driving scenarios without GNSS signals. It is shown that the proposed system significantly outperformed the INS-only system, with an average RMSE improvement of around 93% and 58% in the horizontal and the up directions, respectively.

Design of Light, Small, and High-precision Digital Servo Clock for LEO Constellation

Modern low earth orbit (LEO) satellite systems have placed increasingly stringent requirements on spaceborne time-frequency systems in the fields of constellation autonomous management, distributed synthetic aperture radar (SAR) imaging, and navigation time service. Admittedly, the original high-stability constant-temperature crystal oscillator and high-stability temperature-compensated crystal oscillator can no longer comply with the indicator requirements of clock source frequency characteristics and clock synchronization accuracy. Likewise, traditional satellites use atomic clocks (rubidium atomic clocks and cesium atomic clocks). On account of their inherent characteristics, the ground receiving system needs to observe and count the clock parameter characteristics for a long time after the satellite enters orbit and frequently modify the future working state parameters. The features of being expensive and bulky increase the operation and maintenance costs of the ground system and the complexity of engineering implementation. In this paper, a kind of light and small high-precision digital servo clock based on the deep coupling of the global navigation satellite system (GNSS) is designed for the LEO satellite constellation. The tame clock is deeply coupled with the GNSS navigation receiver, and the high precision time-frequency difference between the local clock frequency source and GNSS is obtained by using the carrier and pseudo-range measurement information of visible satellite and carrier phase measurement information of the receiver. It not only utilizes the short-term high stability of OCXO but also employs the long-term time-frequency maintenance ability of GNSS so that the tamed OCXO is locked in GNSS for a long time, which can achieve frequency calibration of local clock sources and long-term output to various space-borne systems. The system is small in size and low in power consumption, with output frequency stability better than 3E-13 (10000 s), accuracy better than ±2E-13, and timing accuracy better than ±10 ns, which is comparable to the key technical indexes of spaceborne miniaturized rubidium clocks, in line with the current demand of miniaturization, low power consumption, and low cost of LEO constellations.

An enhanced outlier processing approach based on the resilient mathematical model compensation in GNSS precise positioning and navigation

The abnormal measurements are widely existent in Global Navigation Satellite System (GNSS) precise positioning and navigation mainly because of the diffraction, reflection, refraction, and even non-line-of-sight reception. However, when multiple outliers exist in GNSS measurements, traditional methods including test procedure or robust estimation usually cannot work well. This study proposed an enhanced outlier processing approach based on the resilient mathematical model compensation. Specifically, first, to avoid excessive deletion, the total number of measurements is considered in the adaptive test procedure with the help of a scale factor. Second, in adaptive robust estimation, the total number of remaining measurements is also considered, thus making it more compatible with the adaptive test procedure. In addition, to overcome the potential inappropriate reweighting operator, different shrinking factors are adopted for code and phase measurements according to their precision, respectively. To verify the effectiveness of the proposed method, one static monitoring experiment and one kinematic vehicle experiment were conducted, where the method without outlier processing, traditional test procedure, traditional robust estimation, and the proposed method were all used. For the static experiment, the ambiguity resolution and positioning solutions of the proposed method perform best. The positioning accuracy of the float and fixed solutions can be improved by approximately 67.4% and 77.6% on average under challenging environments, respectively. For the kinematic experiment, the performance is also the best in terms of positioning availability and accuracy by using the proposed method.

A comparison analysis on forward error correction technology: a future perspective for GNSS

PurposeThe main aim of this study is to elaborately examine the error correction technology for global navigation satellite system (GNSS) navigation messages and to draw a conceptual decision support framework related to the modernization of the GNSS and other systems.Design/methodology/approachThe extensive simulation model developed in Matrix Laboratory (MATLAB) is used to evaluate the performance of forward error correction (FEC) codes such as Hamming, Bose–Chaudhuri–Hocquenghem, convolutional, turbo, low-density parity check (LDPC) and polar codes under different levels of noise.FindingsThe performance and robustness of the aforementioned algorithms are compared based on the bit length, complexity and execution time of the GNSS navigation message. In terms of bit error rate, LDPC coding exhibits more ability in the robustness of the navigation message, while polar code gives better results according to the execution time.Practical implicationsIn view of future new GNSS signals and message design, the findings of this paper may provide significant insight into navigation message modernization and design as an important part of GNSS modernization.Originality/valueTo the best of the authors’ knowledge, this is the first study that conducts a direct comparison of various FEC algorithms on GNSS navigation message performance against noise, taking into consideration turbo and newly developed polar codes.

Editorial for Special Issue “Precise GNSS Positioning and Navigation: Methods, Challenges, and Applications”

The Global Navigation Satellite System (GNSS) can provide users with high-precision positioning information continuously and benefits all walks of life, e [...]

Artificial Neural Networks for Navigation Systems: A Review of Recent Research

Several machine learning (ML) methodologies are gaining popularity as artificial intelligence (AI) becomes increasingly prevalent. An artificial neural network (ANN) may be used as a “black-box” modeling strategy without the need for a detailed system physical model. It is more reasonable to solely use the input and output data to explain the system’s actions. ANNs have been extensively researched, as artificial intelligence has progressed to enhance navigation performance. In some circumstances, the Global Navigation Satellite System (GNSS) can offer consistent and dependable navigational options. A key advancement in contemporary navigation is the fusion of the GNSS and inertial navigation system (INS). Numerous strategies have been put out recently to increase the accuracy for jamming, GNSS-prohibited environments, the integration of GNSS/INS or other technologies by means of a Kalman filter as well as to solve the signal blockage issue in metropolitan areas. A neural-network-based fusion approach is suggested to address GNSS outages. The overview, inquiry, observation, and performance evaluation of the present integrated navigation systems are the primary objectives of the review. The important findings in ANN research for use in navigation systems are reviewed. Reviews of numerous studies that have been conducted to investigate, simulate, and integrate navigation systems in order to produce accurate and dependable navigation solutions are offered.

Urban Road Safety Prediction: A Satellite Navigation Perspective

Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure safe driving of automated vehicles, the vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, etc.). Such information can be derived from the roads' physical properties, vehicle's capabilities, and weather conditions. From a GNSS-based navigation perspective, the reliability of GNSS signals in different locales, which is heavily dependent on the road layout within the surrounding environment, is crucial to ensure safe automated driving. An urban road environment surrounded by tall objects can significantly degrade the accuracy and availability of GNSS signals. This article proposes an approach to predict the reliability of GNSS-based navigation to ensure safe urban navigation. Satellite navigation reliability at a given location and time on a road is determined based on the probabilistic position error bound of the vehicle-mounted GNSS receiver. A metric for GNSS reliability for ground vehicles is suggested, and a method to predict the conservative probabilistic error bound of the GNSS navigation solution is proposed. A satellite navigation reliability map is generated for various navigation applications. As a case study, the reliability map is used in the proposed optimization problem formulation for automated ground vehicle safety-constrained path planning.

Learning-Enhanced Adaptive Robust GNSS Navigation in Challenging Environments

Global Navigation Satellite System (GNSS) is the widely used technology when it comes to outdoor positioning. But it has severe limitations with regard to safety-critical applications involving unmanned autonomous systems. Namely, the positioning performance degrades in harsh propagation environment such as urban canyons. In this letter we propose a new algorithm for GNSS navigation in challenging environments based on robust statistics. M-estimators showed promising results in this context, but are limited by some fixed hyper-parameters. Our main idea is to adapt this parameter, for the Huber cost function, to the current environment in a data-driven manner. Doing so, we also present a simple yet efficient way of learning with satellite data, whose number may vary over time. Focusing the learning problem on a single parameter enables to efficiently learn with a lightweight neural network. The generalization capability and the positioning performance of the proposed method are evaluated in multiple contexts scenarios (open-sky, trees, urban and urban canyon), with two distinct GNSS receivers, and in an airplane ground inspection scenario. The maximum positioning error is reduced by up to 68% with respect to M-estimators.

Neural network-based ionospheric modeling and predicting—To enhance high accuracy GNSS positioning and navigation

As a highly variable space plasma, the sophisticated monitoring and expression of the ionosphere are critically important for the performance of Global Navigation Satellite System (GNSS). To satisfy the requirement of more precise positioning and navigation, it is necessary to model and predict the ionosphere in a more accurate way. In this paper, the potential advantages of neural networks in ionosphere modeling and predicting are explored. Radial Basis Function Neural Network (RBF-NN) is used for modeling and the Long- and Short-Term Memory Neural Network (LSTM-NN) is used for predicting. Results validate that performance of the RBF-NN is superior to the traditional spherical harmonic and polynomial model. The average root mean square error of one-day modeling residuals by RBF-NN is 1.2951 Total Electron Content Unit (TECU, 1 TECU = 1016 electron/m2), which is reduced by 0.4172 and 0.2710 TECU respectively, compared with the traditional spherical harmonic and polynomial model. The predicting results show that the LSTM-NN has better short-time performance, compared with the finite Fourier series model and the autoregressive integrated moving average model. Finally, one-day predicted products are used as priori constraints for single-frequency static and dynamic precise point positioning (PPP). Positioning results show that constraint effect of the predicted products is equivalent to that of the International GNSS Services (IGS) final products. Compared with the results without ionospheric constraint, the PPP convergence time is obviously reduced, and the positioning performance in the horizontal direction is significantly improved.

A Novel Method to Estimate Multi-GNSS Differential Code Bias without Using Ionospheric Function Model and Global Ionosphere Map

Global navigation satellite system (GNSS) differential code bias (DCB) is one of main errors in ionospheric modeling and applications. Accurate estimation of multiple types of GNSS DCBs is important for GNSS positioning, navigation, and timing, as well as ionospheric modeling. In this study, a novel method of multi-GNSS DCB estimation is proposed without using an ionospheric function model and global ionosphere map (GIM), namely independent GNSS DCB estimation (IGDE). Firstly, ionospheric observations are extracted based on the geometry-free combination of dual-frequency multi-GNSS code observations. Secondly, the VTEC of the station represented by the weighted mean VTEC value of the ionospheric pierce points (IPPs) at each epoch is estimated as a parameter together with the combined receiver and satellite DCBs (RSDCBs). Last, the estimated RSDCBs are used as new observations, whose weight is calculated from estimated covariances, and thus the satellite and receiver DCBs of multi-GNSS are estimated. Nineteen types of multi-GNSS satellite DCBs are estimated based on 200-day observations from more than 300 multi-GNSS experiment (MGEX) stations, and the performance of the proposed method is evaluated by comparing with MGEX products. The results show that the mean RMS value is 0.12, 0.23, 0.21, 0.13, and 0.11 ns for GPS, GLONASS, BDS, Galileo, and QZSS DCBs, respectively, with respect to MGEX products, and the stability of estimated GPS, GLONASS, BDS, Galileo, and QZSS DCBs is 0.07, 0.06, 0.13, 0.11, and 0.11 ns, respectively. The proposed method shows good performance of multi-GNSS DCB estimation in low-solar-activity periods.

Development and evaluation of a generalized model of RAIM availability for single-, dual- and multi-satellite faults

Receiver autonomous integrity monitoring (RAIM) fault monitoring and identification requires first to judge the geometric distribution of currently visible satellites based on performance indicators and to determine whether it is suitable for integrity monitoring, that is, to judge the availability of the RAIM algorithm. With the rapid development of the global navigation satellite system (GNSS), the possibility of dual-satellite faults and multiple-satellite faults in GNSS navigation and positioning has greatly increased. The existing literature mainly focuses on RAIM availability evaluation for single-satellite fault or dual-satellite faults, ignoring the occurrence possibility of multiple-satellite faults, and thus there is no unified RAIM availability evaluation model. To this end, we use the characters of the quadratic matrix to derive a generalized model of RAIM availability evaluation, and the model is suitable for situations of single-, dual- and multi-satellite faults. Based on the observation data of Beidou Navigation Satellite System (BDS) from 332 International GNSS Service (IGS) stations distributed globally on 24 March 2021 and the global ephemeris file, the global RAIM availability of BDS is evaluated. The results show that the generalized model of RAIM availability evaluation has a rigorous derivation process, strong applicability and easy implementation. When the cutoff elevation angle is greater than 5°, the average number of BDS visible satellites at 332 IGS stations worldwide is 12.289 and the average geometry dilution of precision value is 8.090. This can fully meet RAIM availability evaluation and integrity monitoring requirements at most IGS stations and regions. The RAIM availability of triple-satellite faults is lower than that of dual-satellite faults, and the RAIM availability of dual-satellite faults is lower than that of single-satellite fault. For the three different faults, the RAIM availability cannot fully satisfy the four stages in aviation all over the world. Among them, the RAIM availability is highest in the en-route (oceanic/continental low density) stage for all three types of faults, which is 93.146%, 74.848%, and 67.576%, respectively, while the non-precision approach stage has the lowest RAIM availability, which is 78.193%, 13.636%, 3.333% for the three types of faults, respectively.

The GNSS NavAer INCT Project Overview and Main Results

Air navigation is increasingly dependent on the use of Global Navigation Satellite Systems (GNSS). It allows the determination of the aircraft’s position in all phases of the flight and brings many advantages. Although GNSS navigation results in gains, the radio signals from these systems are strongly influenced by the ionospheric environment. It introduces errors that can affect the accuracy, integrity, availability and continuity requirements established by the International Civil Aviation Organization (ICAO). The ionospheric layer has different behaviors depending on the latitude, time of day, season of the year, geomagnetic activity and solar cycle. Since Brazil is located in a region of low latitudes, it experiences a series of unique challenges when compared to regions of mid-latitudes. For this reason, the application of GNSS-based technologies in aviation over the Brazilian territory requires an in-depth assessment of the ionosphere effects. Therefore, the Instituto Nacional de Ciência e Tecnologia (INCT) named GNSS Technology for Supporting Air Navigation was formed in 2017 to better assess the ionosphere impacts and assist government agencies and companies in the development of safe air navigation procedures over Brazil in a near future. This paper presents the most relevant advances achieved so far within this multidisciplinary project that involves Brazilian research centers and universities.

Predictive Model of Multipath Effect Contribution to GNSS Positioning Error for GNSS-based Applications in Transport and Telecommunications

Global Navigation Satellite System (GNSS)-based applications rely on the quality of the GNSS position, navigation, and timing (PNT) services, accomplished through measurement and processing of satellite signals propagation characteristics in a process commonly known as satellite navigation. GNSS positioning performance is in the foundation of the quality of service of GNSS-based applications including the growing number of them in transport, traffic and Intelligent Transport Systems segments, thus a need for a common and independent approach. Here, we propose a novel method for the assessment of the contribution of a single cause to the over-all GNSS positioning error. Proposed method is demonstrated in the case of the GNSS multipath effects, resulting with the experimental predictive model of the direct multipath contribution to GNSS positioning error. The predictive models developed in this research is aimed at deployment in the GNSS positioning performance assessment for GNSS-based applications in transport and telecommunications.

Multi‐Instrument Investigation of the Impact of the Space Weather Events of 6–10 September 2017

AbstractWe analyzed the space weather events of 6–10 September 2017 using the multi‐instrument approach. We focused on the four X‐class flares which emanated from the Active Region AR 12673 and the Ground Induced Currents hazard associated with the geomagnetic storm of 7–8 September 2017. The flare effect on the equatorial electrojet (EEJ) recorded on board the SWARM satellite and on the horizontal component of the geomagnetic field (H) records of ground‐based magnetometers was further examined. During the X2.2/X1.3 flares of 6/7 September, the maximum percentage Global Navigation Satellite System (GNSS) vertical Total Electron Content (VTEC) increase was 6.9%/5.0% in Dakar/Porto Velho. During the X9.3/X8.2 flare of 6/10 September it was 7.9%/18.8% in Ascension Island/Kourou. The strongest Solar Flare Effect occurred in Mbour and Kourou during the respective flare. However, the highest EEJ increase was observed during the X2.2 and X9.3 flares. Interestingly, the X.9.3 flare resulted in a stronger ionospheric response than the X8.2 flare. Furthermore, global TEC map showed a higher response in the African and South American longitude during the respective event. The total radio fade‐out lasted from 30 to 90 min at the Hermanus and Sao Luis ionosondes during the flares, while the risk level to critical ground infrastructures based on the geomagnetically induced currents hazard was very low risk. Our results highlight the potential GPS positioning errors induced by sudden increase in TEC and the loss of high‐frequency communication and GNSS navigation signals associated with these solar events.

Development and evaluation of the refined zenith tropospheric delay (ZTD) models

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.