A quality evaluation of precise products for BDS-3 constellations is presented for the first time in this contribution. Then, the tropospheric delay retrieval and positioning performance of BDS-3 precise point positioning (PPP) solutions using the precise products (gbm, wum, iac, sha, cnt) with observations from 24 stations from DOY 280 to 317 in 2020 was comprehensively investigated. The orbit comparisons present consistencies of 0.09–0.22 m for the C19–C37 satellites and of 0.5–1.2 m for the C38–C46 satellites among the final products. The standard deviation (STD) values of the clock differences of iac showed the best agreement with those of gbm, followed by wum, sha. The clock differences performance of cnt was the worst. For BDS-3 PPP solutions with five Analysis centers (ACs) products, the median convergence times of static PPP mode incorporating the gbm, wum, iac, sha, and cnt products were 31.0, 33.5, 34.5, 37.8, and 72.0 min, respectively; the median convergence times of kinematic PPP model incorporating the same products were 40.5, 41.0, 50.5, 55.0, and 94.0 min, respectively. The positioning accuracies in the static and kinematic modes were approximately 1–4 cm, 2–6 cm in the horizontal and vertical components, respectively. With the final products in kinematic mode, the performance of PPP with real-time products (cnt) is poorer than all PPP with final products. The median of ZTD accuracies of the five products gbm, wum, iac, sha, and cnt were 7.84, 7.58, 7.04, 7.19, and 10.1 mm, respectively, and the accuracy differences were very small among five AC products (gbm, wum, iac, sha).

Single-frequency (SF) receivers are much cheaper than that of dual-frequency (DF). Even though DF precise point positioning (PPP) is nowadays applied in the time community, the cost of equipment is one of the key considerations for time users. Furthermore, the hardware delay calibration of single-frequency devices is simpler than that of dual-frequency devices. In addition, there is no literature to study real-time SF PPP time transfer. In this contribution, the possibility of time transfer using SF PPP was studied. The Un-combined SF PPP was employed for time transfer with ionospheric-constraint using real-time precise products. In this case, 18 multi-GNSS experiment (MGEX) stations and one time lab station were used to study real-time SF PPP time transfer using GPS, Galileo and BDS-2/3 satellites with 20-day. The results suggested that real-time single-frequency PPP can meet time transfer. the standard deviation (STD) of the clock difference obtained from GPS-only, Galileo-only and BDS-2/3 single-frequency PPP are about (0.51, 0.54, 0.91) ns, respectively. The frequency stability of real-time single-frequency PPP can achieve (1E-12, 1E-13, 1E-13) level at short-term and (1E-13, 1E-13, 1E-14) level at long-term, respectively, for BDS-2/3, Galileo-only and GPS-only based.

BDS-3 is now providing global positioning, navigation and timing (PNT) services. BDS-3 has new B1C, B2a and B2b signals compared to BDS-2. This work presents two single-frequency (SF) PPP time transfer models using BDS-3, B1C and B1I observations, and studies the performance of BDS-3 SF PPP time transfer by using 30-day data of 10 globally distributed stations from a multi-GNSS experiment (MGEX). We found that the ionospheric constraint SF PPP (SF1) time transfer model outperforms the method of SF PPP with the receiver clock offset at first epoch as the datum (SF2). Importantly, the statistical uncertainty of SF1 was less than 1 nanosecond, with (0.75, 0.71) ns in the average scheme for all time-links, using both B1I and B1C observations, respectively. The frequency stability of SF1 with B1C observations was improved from 1.73% to 13.04% in the short-term and from 0.88% to 17.49% in the long term, compared to that of B1I for all time-links. Hence, SF1 with B1C observations was recommended for SF PPP time transfer.

Due to the fragility of single-sensor positioning technology in complex scenarios, especially in complex urban areas, multi-sensor positioning technology is becoming increasingly popular. To further improve the robustness of the positioning system by fully utilizing the information from various sensors, this article proposes a differential-GNSS-visual-inertial navigation system (DGVINS) that tightly fuses differential global navigation satellite system (GNSS), vision and inertial information to provide accurate, robust and seamless position information for intelligent navigation applications. DGVINS effectively utilizes all sensor measurements within the factor graph optimization framework. When using the carrier phase of GNSS, single-epoch ambiguity optimization is employed to prevent cycle slip detection and adapted to complex environments. We conducted experiments on public datasets with various features and compared the performance of simple differential-GNSS (DGNSS), DGNSS + Inertial, and the state-of-the-art GNSS-visual-inertial navigation systems. We also compared the performance of different combinations of GNSS differential factors in various environments. Due to the superiority of differential GNSS and its appropriate integration with visual and inertial measurements, the experimental results demonstrate that DGVINS exhibits significant improvements in accuracy, stability, and continuity in both GNSS-challenged and vision-challenged environments.

China is promoting the construction of an integrated positioning, navigation, and timing (PNT) systems with the BeiDou Navigation Satellite System (BDS) as its core. To expand the positioning coverage area and improve the positioning performance by taking advantage of device-to-device (D2D) and self-organizing network (SON) technology, a BDS/SON integrated positioning system is proposed for the fifth-generation (5G) networking environment. This system relies on a combination of time-of-arrival (TOA) and BeiDou pseudo-range measurements to effectively supplement BeiDou signal blind spots, expand the positioning coverage area, and realize higher precision in continuous navigation and positioning. By establishing the system state model, and addressing the single-system positioning divergence and insufficient accuracy, a robust adaptive fading filtering (RAF) algorithm based on the prediction residual is proposed to suppress gross errors and filtering divergence in order to improve the stability and accuracy of the positioning results. Subsequently, a federated Kalman filtering (FKF) algorithm operating in fusion-feedback mode is developed to centrally process the positioning information of the combined system. Considering that the prediction error can reflect the magnitude of the model error, an adaptive information distribution coefficient is introduced to further improve the filtering performance. Actual measurement and significance test results show that by integrating BDS and SON positioning data, the proposed algorithm realizes robust, reliable, and continuous high precision location services with anti-interference capabilities and good universality. It is applicable in scenarios involving unmanned aerial vehicles (UAVs), autonomous driving, military, public safety and other contexts and can even realize indoor positioning and other regional positioning tasks.

GNSS Precise Point Positioning (PPP) technology has been extensively applied to post-processing international comparisons between UTC/TAI times and real-time time transfer, predominantly in static configurations. However, with the swift advancement of intelligent and unmanned systems, there is an urgent need for research into kinematic time transfer. This paper introduces a kinematic model Galileo/GPS integrated PPP time transfer approach leveraging the Galileo High Accuracy Service (HAS). The study utilized observational data from seven stations spanning 22 days. The findings indicate that under static conditions, GPS, Galileo, and Galileo/GPS PPP, when supported by the Galileo HAS, can achieve time transfer with sub-nanosecond precision. In kinematic scenarios, the accuracy of single-system PPP time transfer is comparatively lower, with frequent re-convergence events leading to significant accuracy degradation (exceeding 1 ns). However, in cases where re-convergence is infrequent due to a limited number of satellites, sub-nanosecond time transfer is still attainable. The Galileo/GPS integrated PPP time transfer effectively mitigates the issue of re-convergence, ensuring sub-nanosecond accuracy across all links (0.48 ns). Consequently, it is recommended to employ a multi-system integration approach for kinematic PPP time transfer, particularly when utilizing the Galileo HAS. In terms of frequency stability, GPS, Galileo, and Galileo/GPS PPP demonstrate short-term stability (over 960 s) of (5.29 × 10⁻¹³, 3.34 × 10⁻¹³, and 1.60 × 10⁻¹³), respectively, and long-term stability (over 15,360 s) of (1.49 × 10⁻¹³, 1.02 × 10⁻¹³, and 4.06 × 10⁻¹⁴), respectively.