Global Positioning System Navigation Research Articles

Performance analysis of SBAS ephemeris corrections and integrity algorithms in China region

Satellite Based Augmentation Systems (SBASs) improve the positioning accuracy and integrity by broadcasting to the civil aviation community the corrections and integrity parameters. A snapshot algorithm based on the minimum variance estimation is investigated in this study to calculate the satellite clock and orbit corrections. A chi-square test is performed on the remaining errors in the corrected ephemeris to guarantee the integrity. User Differential Range Error (UDRE) and scaling matrix contained in Message Type 28 are derived using the covariance information based on the assumption that one of the reference stations failed. A software package is developed and applied in the real data collected at 26 stations. International GNSS (Global Navigation Satellite System) Service (IGS) precise clock and orbit products are taken as the references to assess the accuracy of corrections. For both Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), the range accuracy of 0.10 m can be achieved with the employment of the derived corrections. No obvious performance difference between GPS and BDS is found. UDREs for all visible satellites are generated with the maximum index of 12 and minimum index of 3. The geometric range differences calculated with IGS precise products and broadcast ephemeris are employed to assess the integrity of UDRE. It is found that the UDRE is able to bound the residuals with 99.9% confidence which meet the requirement of aviation users. With ionospheric delay corrected by Global Ionosphere Map (GIM), the positioning accuracy of 0.98 m with GPS corrections and 0.80 m with multi-constellation augmentation can be achieved which indicates a significant improvement of GPS standalone results.

Geographic Information System Locating Hospitals and Police Stations Based on Android

Currently technology is developing rapidly. By using a geographic information system and internet service media navigation system or GPS (Global Positioning System) found on the android smartphone platform, Google Maps and Google Services which are virtual map products, free and online. Where people can access maps anywhere and anytime when needed. However, the google map does not show the location of the police station and hospital as a whole, therefore it is necessary to develop a geographic information system (GIS) for the location of police stations and hospitals in the Sidoarjo Region. In this study, researchers created an Android-based automatic geographic information system for finding the location of hospitals and police stations.

ANALYSIS OF POSITIONING DEVIATION BETWEEN BEIDOU AND GPS BASED ON NATIONAL REFERENCE STATIONS IN CHINA

Abstract. In order to probe into the characteristics of positioning deviation between Beidou Navigation Satellite System (Beidou) and global positioning system (GPS), and investigate possible contribution of Beidou data to refinement of global coordinate system, refined calculation is made on observation data of 240 national reference stations that are distributed uniformly across China on the whole in this study. These stations support satellite signals of four global navigation satellite systems, including Beidou, GPS, GLONASS and Galileo, and a 5-year time span from 2016 to 2020 is adopted. In this study, PPP is calculated based on GPS data and Beidou single system data in no-difference resolution network mode, and accurate coordinates of national reference stations in two processing modes are obtained. Analysis of difference between the calculations based on Beidou data and on GPS data shows that the consistency between Beidou and GPS positioning results reaches about 5 mm in the east and in the north, and about 1.3 cm in the height direction.

Tropical cyclone-induced periodical positioning disturbances during the 2017 Hato in the Hong Kong region

The tropospheric delay is an important error source in the Global Positioning System (GPS) positioning and navigation applications. Although most of the tropospheric delays can be removed in the double-differencing (DD) positioning mode, their remaining residuals can still contaminate the positioning accuracy and become unpredictable when tropospheric condition encounters severe variations such as during a tropical cyclone (TC) event. We investigated the positioning performance of five baselines with lengths ranging from 7.8 to 49.9 km during the 2017 TC Hato. The results showed that the TC Hato brought a significant disturbance to the GPS baseline positioning results, particularly in the vertical (up) component. The TC Hato started to affect Hong Kong and the root mean squares (RMS) of GPS positioning errors increased dramatically from about 30 to 140 mm, when it was at a distance of 400–600 km from Hong Kong on August 22, 2017. We found that the vertical positioning errors on that day have the major periods: 2.7 h, 3.0 h, 3.4 h, 4.0 h, and 4.8 h. Examining the wet and hydrostatic parts of the tropospheric delays via the continuous wavelet spectral analysis, we found that the periodical variation of the positioning results on August 22 was caused by the periodical variation of the precipitable water vapor (PWV). The variation of differenced PWV between two baseline stations had consistent periods of 2–5 h. Besides, the periods of differenced PWV time series are in good agreement with the spiral rainband in the TC. This finding suggests that the TC Hato induce periodical PWV variations at two GPS stations of baseline, which resulted in GPS positioning errors of the same periods.

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

New Methodology for Computing the Aircraft’s Position Based on the PPP Method in GPS and GLONASS Systems

In the field of air navigation, there is a constant pursuit for new navigation solutions for precise GNSS (Global Navigation Satellite System) positioning of aircraft. This study aims to present the results of research on the development of a new method for improving the performance of PPP (Precise Point Positioning) positioning in the GPS (Global Positioning System) and GLONASS (Globalnaja Nawigacionnaja Sputnikovaya Sistema) systems for air navigation. The research method is based on a linear combination of individual position solutions from the GPS and GLONASS systems. The paper shows a computational scheme based on the linear combination for geocentric XYZ coordinates of an aircraft. The algorithm of the new research method uses the weighted mean method to determine the resultant aircraft position. The research method was tested on GPS and GLONASS kinematic data from an airborne experiment carried out with a Seneca Piper PA34-200T aircraft at the Mielec airport. A dual-frequency dual-system GPS/GLONASS receiver was placed on-board the plane, which made it possible to record GNSS observations, which were then used to calculate the aircraft’s position in CSRS-PPP software. The calculated XYZ position coordinates from the CSRS-PPP software were then used in the weighted mean model’s developed optimization algorithm. The measurement weights are a function of the number of GPS and GLONASS satellites and the inverse of the mean error square. The obtained coordinates of aircraft from the research model were verified with the RTK-OTF solution. As a result of the research, the presented solution’s accuracy is better by 11–87% for the model with a weighting scheme as a function of the inverse of the mean error square. Moreover, using the XYZ position from the RTKLIB program, the research method’s accuracy increases from 45% to 82% for the model with a weighting scheme as a function of the inverse of the square of mean error. The developed method demonstrates high efficiency for improving the performance of GPS and GLONASS solutions for the PPP measurement technology in air navigation.

Development of an Automatic Contactless Thermometer Alert System Based on GPS and Population Density

In today's out-breaking Covid-19 circumstance, treatments are preferred to be contactless. Social distancing has become a mandate in order to prevent disease spreading. In such a scenario, checking the body temperature is preferable to be made contactless because it helps the doctors and social workers to stay away from the symptomatic patients. Infrared (IR) contactless thermometers are employed in measuring the temperature while preventing direct contact with the body. Improved functionalities in the contactless thermometer can provide accurate precision in measurements and calculations. Technological advancement in pharmacy has cohesively improved over time. Coupling Machine Learning (CML) will revolutionize the process of testing. The demand for automated temperature test equipment is likely to grow at a significant pace, with the continuous advancements in technology and the adoption of ATE (Automated Test Equipment). The Global Positioning System (GPS) easy tracking and navigation can be used for easy tracking. Population density can be used to calculate the amount of population in a particular area. The proposed automatic contact-less thermometer system has the potential to replace the traditional temperature measuring techniques and safeguard from human-to-human transmission diseases.

Simulations suggest that navigation software may not be as efficient as expected for city traffic.

We suggest a theoretical framework to study the dynamics of an open city, with cars entering at a certain rate and leaving as they reach their destinations. In particular, we assess through simulations some unexpected consequences of the massive use of GPS (global positioning system) navigation systems in the overall dynamics. One of our main interest is to identify what type of measurements would be the most relevant for an experimental study of this system, specifically, the ones useful for city traffic administrators. To do so, we solve the microdynamics using a cellular automaton model considering three different navigation strategies based on the minimization of the individual paths (unweighted strategy) or travel times (weighted strategies). Although the system is inherently stochastic, we found in our simulations an equivalent saddle-node bifurcation for all strategies where the input rate acts as a bifurcation parameter. There is also evidence of additional bifurcations for travel time minimization based strategies. Although we found that weighted strategies are more efficient in terms of car motion, there is a destabilization phenomenon that makes, in an unexpected way, a variation of the unweighted strategy more optimal at certain densities from the fuel efficiency of the overall city traffic point of view. These results bring new insight into the intrinsic dynamics of cities and the perturbations that individual traffic routing can produce on the city as a whole.

Altitude on Cartographic Materials and Its Correction According to New Measurement Techniques

Determining the correct height of mountain peaks is vital for tourism, but it is also important as a reference point for devices equipped with GPS (Global Positioning System), e.g., watches or car navigation systems. The peak altitude data are part of geographic and geodetic information. As more modern technologies and equipment become available, their precisions should increase. However, verification of peak heights is usually only conducted for the highest, well-known mountains—lower peaks or mountain passes are rarely verified. Therefore, this study focuses on an investigation of 12 altitude points on a section of the longest and most famous touristic trail in Poland (the Main Beskid Trail), located in the Orava–Żywiec Beskids Mts (Mountains). The aim of this research is to measure and verify the heights of the 12 selected mountain peaks, in addition to evaluating the chosen methods based on the quality of the obtained data and determining their suitability and opportunities for use in further research. Measurements were obtained at the most specific height points—on the 12 highest points of the summits. This study compares two modern measurement techniques: the global navigation satellite system (GNSS) and light detection and ranging (LiDAR). The obtained results were later compared with those widely used on the internet and in printed materials (period covered: 1884–2015). This analysis demonstrates that lesser-known objects are rarely the subject of remeasurement and significant altitude errors may occur, primarily because the heights originated from a source in the past when modern methods were not available. Our findings indicate that the heights of the peaks presented in cartographic materials are inaccurate. The assumed heights should be corrected by direct measurements using modern techniques.

A Closed-Form Localization Method Utilizing Pseudorange Measurements From Two Nonsynchronized Positioning Systems

In a time-of-arrival (TOA) or pseudorange based positioning system, user location is obtained by observing multiple anchor nodes (AN) at known positions. Utilizing more than one positioning systems, e.g., combining Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), brings better positioning accuracy. However, ANs from two systems are usually synchronized to two different clock sources. Different from single-system localization, an extra user-to-system clock offset needs to be handled. Existing dual-system methods either have high computational complexity or sub-optimal positioning accuracy. In this paper, we propose a new closed-form dual-system localization (CDL) approach that has low complexity and optimal localization accuracy. We first convert the nonlinear problem into a linear one by squaring the distance equations and employing intermediate variables. Then, a weighted least squares (WLS) method is used to optimize the positioning accuracy. We prove that the positioning error of the new method reaches Cramer-Rao Lower Bound (CRLB) in far field conditions with small measurement noise. Simulations on 2D and 3D positioning scenes are conducted. Results show that, compared with the iterative approach, which has high complexity and requires a good initialization, the new CDL method does not require initialization and has lower computational complexity with comparable positioning accuracy. Numerical results verify the theoretical analysis on positioning accuracy, and show that the new CDL method has superior performance over the state-of-the-art closed-form method. Experiments using real GPS and BDS data verify the applicability of the new CDL method and the superiority of its performance in the real world.

A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes.

We consider how to protect Unmanned Aerial Vehicles (UAVs) from Global Positioning System (GPS) spoofing attacks to provide safe navigation. The Global Navigation Satellite System (GNSS) is widely used for locating drones and is by far the most popular navigation solution. This is because of the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. Nevertheless, there are many security threats to GPS navigation. These are primarily related to the nature of the GPS signal, as an intruder can jam and spoof the GPS signal. We discuss methods of protection against this type of attack and have developed an experimental stand and conducted scenarios of attacks on a drone’s GPS system. Data from the UAV’s flight log were collected and analyzed in order to see the attack’s impact on sensor readings. From this we identify a new method for detecting UAV anomalies by analyzing changes in internal parameters of the UAV. This self-diagnosis method allows a UAV to independently assess the presence of changes in its own subsystems indicative of cyber attacks.

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

Unmanned parking method based on cloud platform

With the intelligent development of automobile driving, automatic parking technology is widely concerned by relevant technicians at home and abroad. Automatic parking technology is a method that can realize automatic parking in and out of parking Spaces without manual intervention. At present, automatic parking system has been equipped and used in many models, but there are many problems such as low parking accuracy and unable to effectively identify irregular parking Spaces. In order to solve the above problems, this article is based on a cloud platform developed a driverless car parking method, through the cloud server, GPS (Global Positioning System) and inertial navigation. It can according to the parking information and real-time Positioning planning adjustment path of the vehicle, and controls the complete vehicle posture adjustment, so as to improve the accuracy of parking and parking intelligent identification accuracy and ensure the safety and efficiency in the process of path planning. The effectiveness of the method is verified by simulation experiments with MATLAB software, which can be demonstrated by data.

An Integrated Navigation Method Based on an Adaptive Federal Kalman Filter for a Rice Transplanter

HighlightsAn integrated GPS/INS/VNS navigation system was developed to improve navigation accuracy.An adaptive federal Kalman filter with information distribution factors was used to fuse navigation information.Detection of seedling row lines was achieved based on subregional feature points clustering.A modified rice transplanter was developed as an experimental platform for automatic navigation.Abstract. In this article, an integrated global positioning system (GPS), inertial navigation system (INS), and visual navigation system (VNS) navigation method based on an adaptive federal Kalman filter (KF) is presented to improve positioning accuracy for a rice transplanter operating in a paddy field. The proposed method used GPS/VNS to aid the INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and transplanter tests were conducted to verify the proposed method. Results showed that the proposed method provided accurate and reliable navigation information outputs and achieved better navigation performance compared with single GPS navigation and an integrated method based on a conventional federal KF. Keywords: Federal Kalman filter, GPS/INS/VNS, Information distribution factor, Information fusion, Integrated navigation.

Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.

Improving Linear Projects Georeferencing to Create Digital Models Using UAV Imagery

Global Positioning System (GPS) on Unmanned Aerial Vehicles (UAV) platform relies on Micro Electro Mechanical Systems (MEMS) technology with a precision of 10 m at shooting time at UAV camera stations positions. Nonetheless, obstacles to the GPS signal at the finest flight altitude can prevent accurate camera stations positions retrieval. In this research, three different georeferencing techniques were compared with geometric precision. The first is Direct Georeferencing (DG), which mainly depends on using Navigation GPS onboard without using any Ground Control Points (GCPs). The second is Indirect Georeferencing (IG), which mainly depends on three GCPs used to assist Aero-Triangulation (AT). The third is Modified technique depends on the same three GCPs used in the second method and enhanced location of camera stations usage of the Linear Relation Model (LR Model). The study area was in the south of the Moscow Region, Russia. Threeimaging strips have been taken using the DJI PHANTOM 4 PRO UAV. The accuracy assessment was carried out using image-derived coordinates and checkpoints (CPs) residuals. This study emphasizes that the Modified methodology using enhanced camera stations positions gave better accuracy than using the drone GPS camera stations positions.

A method for automatic detection and characterization of plasma bubbles using GPS and BDS data

Detecting and characterizing Total Electron Content (TEC) depletion is important for studying the ionospheric threat due to the Equatorial Plasma Bubble (EPB) when applying the Ground-Based Augmentation System (GBAS) at low latitudes. This paper develops a robust method to automatically identify TEC depletion and derive its parameters. The rolling barrel algorithm is used to automatically identify the TEC depletion candidate and its parameters. Then, the depletion candidates are screened by several improved techniques to distinguish actual depletions from other phenomena such as Traveling Ionospheric Disturbance (TID) or abnormal data. Next, based on the depletion signals from three triangular receivers, the method derives EPB parameters such as velocity, width and gradient. The time lag and front velocity are calculated based on cross-correlation using TEC depletions and the geometrical distribution of three triangular receivers. The width and gradient of slope are then determined by using TEC depletion from a single receiver. By comparison, both the station-pair method and proposed method depend on the assumption that the EPB morphology is frozen during the short time when the plasma bubble moves between the receivers. However, our method relaxes the restriction that the baseline length should be shorter than the width of slope required by the station-pair. This relaxation is favorable for studying small-scale slope of depletions using stations of a longer baseline. In addition, the accuracy of the width and gradient is free of impact from hardware biases and small-scale disturbance, as it is based only on the relative TEC variation. The method is demonstrated by processing Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) data on 15 August, 2018, in a solar minimum cycle.

Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions-Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014.

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

Statistical Distribution Analysis of Navigation Positioning System Errors-Issue of the Empirical Sample Size.

Positioning systems are used to determine position coordinates in navigation (air, land, and marine). Statistical analysis of their accuracy assumes that the position errors (latitude—δφ and longitude—δλ) are random and that their distributions are consistent with the normal distribution. However, in practice, these errors do not appear in a random way, since the position determination in navigation systems is done with an iterative method. It causes so-called “Position Random Walk”, similar to the term “Random Walk” known from statistics. It results in the empirical distribution of δφ and δλ being inconsistent with the normal distribution, even for samples of up to several thousand measurements. This phenomenon results in a significant overestimation of the accuracy of position determination calculated from such a short series of measurements, causing these tests to lose their representativeness. This paper attempts to determine the length of a measurement session (number of measurements) that is representative of the positioning system. This will be a measurement session of such a length that the position error statistics (δφ and δλ) represented by the standard deviation values are close to the real values and the calculated mean values ( and ) are also close to the real values. Special attention will also be paid to the selection of an appropriate (statistically reliable) number of measurements to be tested statistically to verify the hypothesis that the δφ and δλ distributions are consistent with the normal distribution. Empirical measurement data are taken from different positioning systems: Global Positioning System (GPS) (168′286 fixes), Differential Global Positioning System (DGPS) (864′000 fixes), European Geostationary Navigation Overlay Service (EGNOS) (928′492 fixes), and Decca Navigator system (4052 fixes). The analyses showed that all researched positioning systems (GPS, DGPS, EGNOS and Decca Navigator) are characterized by the Position Random Walk (PRW), which resulted in that the empirical distribution of δφ and δλ being inconsistent with the normal distribution. The size of the PRW depends on the nominal accuracy of position determination by the system. It was found that measurement sessions consisting of 1000 fixes (for the GPS system) overestimate the accuracy analysis results by 109.1% and cannot be considered representative. Furthermore, when analyzing the results of long measurement campaigns (GPS and DGPS), it was found that the representative length of the measurement session differs for each positioning system and should be determined for each of them individually.

Characterization of biases between BDS-3 and BDS-2, GPS, Galileo and GLONASS observations and their effect on precise time and frequency transfer

With the development of the Chinese BeiDou Navigation Satellite System (BDS), from a regional system BDS-2 to a global system BDS-3, more satellites can now be employed in the domain of globally precise time and frequency transfer. However, if the current BDS-3 and BDS-2, Global Positioning System (GPS), Galileo and GLObal NAvigation satellite system (GLONASS) are combined for multi-global navigation satellite system (multi-GNSS) time transfer, the characteristics of inter-system biases (ISBs) remain unclear. In this study, we analyzed the characteristics of ISBs between BDS-3 and BDS-2, GPS, Galileo and GLONASS, and revealed that their ISB series is not constant and that all the daily ISB series of the multi-GNSS show some systematic characteristics among different stations. Therefore, three stochastic models for ISBs of white noise process, random constant process and random walk process expressed as ‘White’, ‘Hour’ and ‘Walk’, respectively, were employed to assess the performance of the time and frequency transfer using the time link accuracy and frequency stability indicators. The results show that the models for the ‘Hour’ and ‘Walk’ schemes are notably better than that for the ‘White’ scheme by 10.4% and 16.8%, respectively, for the least noisy time link of WTZZ-BRCH. Furthermore, the improvements in the ‘Hour’ and ‘Walk’ schemes are 2.1% and 3.5%, respectively, for the TP01-BRCH time link, when compared with that of the ‘White’ scheme. With respect to frequency stability, the ‘Hour’ and ‘Walk’ schemes also perform better in terms of frequency stability than the ‘White’ scheme at different time intervals. Similarly, the improvements over the ‘White’ scheme are 5.05% and 4.97% for the TP01-BRCH time link and are 24.01% and 32.48% for the WTZZ-BRCH time link.