A Tightly Coupled BDS/INS Integrated Positioning Algorithm Based on Triple-Frequency Single-Epoch Observations

The code-phase divergences, which are minimal for GPS, GLONASS, and Galileo satellites, are commonly found in BeiDou Navigation Satellite System (BDS) Geostationary Orbit (GEO), Inclined GeoSynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. Several precise positioning applications which use code observations are severely affected by these code biases. We present an analysis of code bias based on multipath (MP) combination observations. To mitigate the effect of BDS code bias on precise positioning, we proposed a periodical correction method using a low-pass filter for BDS GEO, IGSO and MEO satellites. The auto-correlation of MP series over long periods is analyzed to obtain the periods of the dominant repeating components for three types of BDS satellites. The periods of the dominant daily repeating components are close to 86,160 s for BDS GEO and IGSO satellites while 603,120 s for MEO satellites. The zero phase-shift low-pass filter was used to extract the low-frequency components of MP series and then low-frequency components are applied to mitigate the code bias periodically. The developed correction methods can make a more remarkable improvement for the accuracy of MP series, compared to the current elevation-dependent correction models. Data sets collected at 50 Global Navigation Satellite System (GNSS) ground stations including 15 of the International GNSS Monitoring and Assessment System (iGMAS) and 35 of the Multi-GNSS Experiment (MGEX) stations are employed for this study. To analyze the influence of code bias on precise positioning and validate the effectiveness of the correction methods, some applications such as single point positioning (SPP), wide-lane (WL) ambiguity analysis and Uncalibrated Phase Delays (UPDs) estimation are conducted. After applying the proposal correction method to the code observations, SPP solutions outperform the uncorrected ones in term of positioning accuracy. The positioning accuracy decreased by 0.28 and 0.1 m in the north and east components and the improvements are more significant for the U components decreased by 0.42 m. In addition, the systematic variations of Melbourne-Wübbena (MW) combination are greatly removed and the convergence time of the MW series are decreased. Moreover, significant improvement is also achieved in terms of the usage rate and residuals of UPDs estimation.

Under the high dynamic conditions, Global Navigation Satellite System (GNSS) signals produce great Doppler frequency shifts, which hinders the fast acquisition of signals. Inertial Navigation System (INS)-aided acquisition can improve the acquisition performance, whereas the accuracy of Doppler shift and code phase estimation are mainly determined by the INS precision. The relation between the INS accuracy and Doppler shift estimation error has been derived, while the relation between the INS accuracy and code phase estimation error has not been deduced. In this paper, in order to theoretically analyze the effects of INS errors on the performance of Doppler shift and code phase estimations, the connections between them are re-deduced. Moreover, the curves of the corresponding relations are given for the first time. Then, in order to have a better verification of the INS-aided acquisition, a high dynamic scenario is designed. Furthermore, by using the deduced mathematical relation, the effects of different grade INS on the GNSS (including Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)) signal acquisition are analyzed. Experimental results demonstrate that the INS-aided acquisition can reduce the search range of local frequency and code phase, and achieve fast acquisition. According to the experimental results, a suitable INS can be chosen for the deeply coupled integration.

PPP-RTK which takes full advantages of both Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), is able to provide centimeter-level positioning accuracy with rapid integer Ambiguity Resolution (AR). In recent years, with the development of BeiDou Navigation Satellite System (BDS) and Galileo navigation satellite system (Galileo) as well as the modernization of Global Positioning System (GPS) and GLObal NAvigation Satellite System (GLONASS), more than 140 Global Navigation Satellite System (GNSS) satellites are available. Particularly, the new-generation GNSS satellites are capable of transmitting signals on three or more frequencies. Multi-GNSS and multi-frequency observations become available and can be used to enhance the performance of PPP-RTK. In this contribution, we develop a multi-GNSS and multi-frequency PPP-RTK model, which uses all the available GNSS observations, and comprehensively evaluate its performance in urban environments from the perspectives of positioning accuracy, convergence and fixing percentage. In this method, the precise atmospheric corrections are derived from the multi-frequency and multi-GNSS observations of a regional network, and then disseminated to users to achieve PPP rapid AR. Furthermore, a cascade ambiguity fixing strategy using Extra‐Wide‐Lane (EWL), Wide-Lane (WL) and L1 ambiguities is employed to improve the performance of ambiguity fixing in the urban environments. Vehicle experiments in different scenarios such as suburbs, overpasses, and tunnels are conducted to validate the proposed method. In suburbs, an accuracy of within 2 cm in the horizontal direction and 4 cm in the vertical direction, with the fixing percentage of 93.7% can be achieved. Compared to the GPS-only solution, the positioning accuracy is improved by 87.6%. In urban environments where signals are interrupted frequently, a fast ambiguity re-fixing can be achieved within 5 s. Moreover, multi-frequency GNSS signals can further improve the positioning performance of PPP-RTK, particularly in the case of small amount of observations. These results demonstrate that the multi-frequency and multi-GNSS PPP-RTK is a promising tool for supporting precise vehicle navigation.

In-orbit performance of GNOS on-board FY3-C and the enhancements for FY3-D satellite

A novel method of ambiguity resolution and cycle slip processing for single-frequency GNSS/INS tightly coupled integration system

The performance of navigation can be significantly enhanced by coupling the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) because the characteristics of these two systems complement each other. Among those structures of coupling, Ultra-Tight Coupling (UTC) fully uses the information of both systems, therefore has attracted much attention of researchers. To do researches of UTC, it is reasonable that the simulation platform should be built first. Although there have been several open source simulation platforms for the researches of Tight Coupling (TC), which have become important tools for the analysis of TC, the researches about designing simulation platform of UTC is rare, and the standard of simulation has not been settled either, becoming a barrier of the development of UTC. In this paper, a Software Defined Receiver (SDR) for UTC simulation is proposed. The SDR is derived from the open source code of TC SDR with many modules added, including the GNSS signal generation, transmitting, acquisition, and tracking. The tracking results of receiver are transformed to the errors of pseudo-range, and then used in the Extended Kalman Filter (EKF) for coupling. The output of EKF on the other hand adjusts the INS and the loop of GNSS as feedback. The design of SDR for UTC does not need to simulate the navigation data, hence is easy to be realized. The simulation results are also presented to show the validity of the designed SDR.

Beidou satellite navigation system (BDS) signal is blocked during dynamic positioning which can lead to poor geometry structure of visible satellites and serious multipath effect. In this contribution, the advantages of inertial navigation system (INS), autonomy and short-term accuracy, are used to speed up the BDS ambiguity resolution (AR). However, the accuracy of general INS cannot meet the fixed BDS original carrier ambiguity requirement. BDS can broadcast three frequency observations, so the virtual observations of long wavelength and short wavelength are formed by linear combination, which make the INS precision meet the fixed ambiguity requirement and achieve high precision positioning, respectively. The short wavelength observations ambiguity due to the relatively large noise is difficult to reliably fixed. To solve this problem, Empirical mode decomposition (EMD) can be used to decompose the error of the short wavelength observations into two parts: systematic error and random error. Systematic error is used to correct the short wavelength ambiguity float solution and reduce the ambiguity search space, to achieve a reliable fixed purpose, finally solving the baseline solution. The experimental results show that the algorithm is more fast and reliable than the traditional algorithms, and more suitable for dynamic positioning in the complex environment.

Now a days, Unmanned Aerial Vehicles (UAVs) have been exploited in numerous civil and military aspects such as disaster management and border monitoring. Although most of these UAVs are equipped with Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) reliable integrated system for localization proposes, they are susceptible to lose their GNSS signals under some environmental and artificial challenging circumstances such as, flying in urban areas, jamming, and spoofing. In such complicated scenarios, the navigation solution will degrade rapidly due to the accumulated INS drift errors. Although various aiding sensors such as cameras, and radar are employed to mitigate the INS drift errors, their estimated positioning accuracy is still affected by some factors. This paper presents a nested optimal filter approach (G-H filter, Extended Kalman Filter (EKF)) for UAVs in GNSS denied environment, to enhance the estimated positioning accuracy by integrating the Radar Odometry (RO)/INS/MAG to sustain a reliable navigation solution during GNSS outage. First, the UAV relative velocity is calculated based on image processing approach from the radar range doppler map, then the G-H filter is deployed to improve the accuracy of the vehicle estimated forward velocities based on the vehicle dynamic characteristics to reduce the effect of unrealistic velocity estimate from the radar. These improved velocities are then fused with the Inertial Measurement Unit (IMU), magnetometer, and barometer via EKF to mitigate the positioning RMS errors during the GNSS signals outages. The experimental results demonstrate the proposed system's ability to notably enhance the 3D positioning accuracy during the GNSS signal outage by more than 99% when compared to a stand-alone IMU dead reckoning solution.

With the rapid development of the Beidou satellite navigation system (BDS), assessment of its signal quality and navigation performance becomes more and more important. The pseudo-range multipath, carrier-phase signal-to-noise ratio (SNR) and accuracy of standard point positioning are usually taken as the indicators for assessment. In this research, we compared the signal quality and navigation performance of BDS with other three Global Navigation Satellite System (GPS, GLONASS, Galileo). The results of the Asia-Pacific region show that the pseudo-range multipath error for BDS is similar with GPS whereas the GLONASS code measurements exhibit a higher level. The level of carrier-phase signal-to-noise ratio for Galileo is largest, but similar for BDS, GPS and GLONASS. The standard point navigation and positioning performance of BDS is slightly lower than GPS, but it is better than GLONASS, and the accuracy of BDS, GPS and GLONASS standard point positioning is about 10 m. The pseudo-range multipath error of BDS increases as the satellites elevation decreases, but the carrier-phase signal-to-noise ratio is opposite. The variation of pseudo-range multipath error of BDS GEO satellites is small except for low satellites elevation, but the variation of signal-to-noise ratio of GEO satellites is relatively stable. The variation of multipath and signal-to-noise ratio of BDS IGSO and MEO satellites is relatively stable when the satellites elevation is larger than a certain value.

Because of the special design of BeiDou navigation satellite system (BDS) constellation, the effects of ionospheric scintillation on operational BDS generally are more serious than on the global positioning system (GPS). As BDS is currently providing global services, it is increasingly important to seek strategies to mitigate the scintillation effects on BDS navigation and positioning services. In this study, an improved cycle-slip threshold model is proposed to decrease the high false-alarm rate of cycle-slips under scintillation conditions, thus avoiding the frequent unnecessary ambiguity resets in BDS precise point positioning (PPP) solution. We use one-year (from 23 March 2015 to 23 March 2016) BDS dataset from Hong Kong Sha Tin (HKST) station (22.4°N, 114.2°E; geomagnetic latitude: 15.4°N) to model the cycle-slip threshold and try to make it suitable for three types of BDS satellites and multiple scintillation levels. The availability of our mitigation strategy is validated by using three months (from 1 September 2015 to 30 November 2015) BDS dataset collected at 10 global navigation satellite system (GNSS) stations in Hong Kong. Positioning results demonstrate that our mitigated BDS PPP can prevent the sudden fluctuations of positioning errors induced by the ionospheric scintillation. Statistical results of BDS PPP experiments show that the mitigated solution can maintain an accuracy of about 0.08 m and 0.10 m in the horizontal and vertical components, respectively. Compared with standard BDS PPP, the accuracy of mitigated PPP can be improved by approximately 24.1%, 38.2%, and 47.9% in the east, north, and up directions, respectively. Our study demonstrates that considering different scintillation levels to establish appropriate cycle-slip threshold model in PPP processing can efficiently mitigate the ionospheric scintillation effects on BDS PPP.

Conventional global navigation satellite system (GNSS)/micro-electromechanical strapdown inertial navigation system (MEMS-SINS) tight integration encounters some major limitations in GNSS-challenged areas. It is GNSS signal impairments due to the complex environment surrounding GNSS antenna, that plagues the acquired integration performance, resulting in significant accuracy degradation of MEMS-based inertial navigation. To improve the overall performance of GNSS/MEMS-SINS tightly-coupled (TC) system, this paper proposes the following three-tier approach at different levels without either additional sensors or GNSS base stations: 1) improving the stochastic modeling for both MEMS sensors and GNSS receiver clock errors using Allan variance (AV) analysis and satellite-differencing (SD) at the dynamics level, 2) enhancing the integration by augmenting unambiguous time-differenced carrier phase (TDCP) into Kalman filter at the measurement level, and 3) eliminating the adverse effects of GNSS outliers by devising innovation-based test statistics at the information fusion level. The field test results indicate that the proposed algorithm could improve the overall navigation accuracy by about 42.14% (position), 38.96% (velocity) and 33.56% (attitude) with immunity to both GNSS outliers and cycle slips, compared with conventional TC counterpart. Moreover, it can also significantly reduce the coasting errors and improve the relative position accuracy during relatively long outages. The results demonstrate the effectiveness of the proposed algorithm in reducing the growth of MEMS-SINS error.

In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Accurate train positioning is crucial for ensuring the safety and operational efficiency of intelligent railway systems. Traditional Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration often performs reliably only under ideal conditions. However, in GNSS-denied environments such as tunnels, signal blockage can cause significant positioning errors. To overcome these limitations, this paper proposes a seamless train positioning framework that dynamically switches between Visual Place Recognition (VPR) /INS and GNSS/INS integration. The framework operates adaptively: in tunnel or signal-denied scenarios, where GNSS is unavailable, VPR/INS integration maintains accurate localization; in open-sky environments, GNSS/INS integration ensures continuous positioning. To enable VPR-based localization, a reference image database of the railway line is pre-constructed, and the NetVLAD model is employed to extract robust global image descriptors. A location-guided image retrieval strategy is then introduced to accelerate global feature matching, providing a coarse estimation of the train’s position based on retrieval results. To ensure the accuracy of the matching process, we have implemented a double-check mechanism that accepts matching results only when validated, thereby enhancing overall railway positioning safety. Field experiments conducted on the Lhasa–Nyingchi Railway show that during GNSS signal blockage, the proposed switching system effectively reduces positioning errors, achieving an overall RMS error of approximately 1.03m.

With the rapid development of high-speed trains in the world, how to quickly collect train operation data becomes a significant issue. The paper uses Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and Driver Machine Interface (DMI) information fusion to obtain the position and speed information of the train more accurately. Among these information sources, GNSS and INS can carry out the collection of train position and speed information with and without obstructions, respectively. When the train is running in the open and unobstructed, GNSS can directly obtain train operation information. When the train passes through an environment with obstructions, GNSS and INS work simultaneously to obtain train operation information. The DMI on the train has the timely speed and kilometer mark information of the train. During the measurement, as we can not access to the onboard equipment. The camera is applied to collect the DMI interface. The image is processed to obtain the train data information. In this paper, the train data collected by GNSS/INS equipment is corrected by the Kalman filtering method. The data collected by DMI is filtered by the kinematics theorem and the least square method. What is more, a centralized fusion structure is used to process the DMI, GNSS, and INS data. This result can provide more accurate train time, speed, and kilometer mark information for the train during the operation test.