China Satellite Navigation Conference (CSNC 2024) Proceedings

With the rapid development of dam construction, the demand for dam management and maintenance is rapidly expanding. Dam cracks are the core of dam management and maintenance, and rapid and effective detection of cracks and their spatial location and geometric parameters are the basis for dam maintenance. However, dam safety supervision mainly relies on manual work, which is very labor-intensive and time-consuming, and the detection is not fine enough. Therefore, we propose a new and efficient low-cost dam inspection system - UAV dam crack detection system based on Beidou and LIDAR. The UAV is equipped with LIDAR to obtain a high-precision three-dimensional point cloud of the dam surface, use the filtering algorithm to obtain the height difference and intensity contrast of the same dam surface, and then obtain the candidate point cloud of cracks according to the height difference and intensity contrast, use the maximum entropy threshold segmentation method to extract the candidate point cloud, and then filter and denoise the candidate point cloud based on the trough effect, and extract the cracks and their geometric parameters according to the morphological filtering. The texture information obtained from the conventional optical camera is used to construct a 3D model of the dam, and the cracks are displayed on the 3D model to statistically evaluate the severity of the cracks. The system aims to get more and more accurate information about the location and geometry of dam cracks through the combination of Beidou system and LIDAR, so as to better detect the hidden danger of dam safety, save human and material resources for dam safety management, and make the management more convenient and efficient.

Real-time high-precision positioning and navigation of smart terminals has a huge potential demand in the field of positioning, but for reasons of cost and power consumption, its low-cost global navigation satellite system (GNSS) positioning chips and antennas carrying received satellite observation data are of poor quality, prone to circumferential jumping, and seriously affected by multi-path effects, making it difficult to meet the requirements of high-precision positioning. There is a huge potential demand for real-time high-precision positioning and navigation for smart terminals in the field of positioning. However, for reasons of cost and power consumption, the low-cost global navigation satellite system (GNSS) positioning chips and antennas on which they are mounted receive poor quality satellite observation data, which are prone to circumferential jumping and are seriously affected by multi-path effects, making it difficult to achieve the requirements of high-precision positioning. Meanwhile, with the arrival of 5th Generation Mobile Communication Technology (5G) communication technology, its characteristics of low latency, large bandwidth and large number of connections make it easier to transmit a large amount of GNSS observation data in real time. In order to modify certain smart terminals to meet the demand of high accuracy, combined with 5G high-speed network, A method was proposed to build an plug-in data platform using BeiDou high-precision board + 5G and Bluetooth technology to enhance the accuracy of smart terminal Application (APP) applications. The plug-in data platform is divided into a server module and a user terminal module. The server module continuously receives real-time satellite observation data from the base station. The user terminal module mainly receives the satellite observation data by using the Unicorecomm UB4B0M board, outputs the data from the serial port through the board backplane, then transmits the data to the Arduino microcontroller via the serial to Transistor-Transistor Logic (TLL) module, and finally sends the data to the intelligent terminal APP by the HC-05 Bluetooth module; after the terminal receives the data, the terminal APP submits the time stamp to the server, which matches the satellite observation data of the base station and sends it back to the terminal APP via 5G high-speed network in real time for real-time carrier phase difference calculation. The experimental verification shows that the intelligent terminal plug-in data platform operates stably and correctly, and can achieve centimeter-level navigation and positioning accuracy, which can provide technical reference for the transformation of existing low-precision intelligent terminals and the development of future high-precision intelligent terminals.

The strong L-band solar radio bursts will affect the navigation signals captured and tracked by the Global Navigation Satellite System (GNSS) receivers located in the sunlit hemisphere (sunny side), thus affecting the stability and accuracy of GNSS services. In this paper, an L-band solar radio burst erupted on August 28, 2022 was detected and analyzed based on GNSS Monitor System. Similar to December 13, 2006, the incident affected the GNSS tracking stations in the sunny side to varying degrees. Based on IGS data and low orbit satellite data, the characteristics and influence of the two events are compared. The analysis results show that: (1) solar L-band radio bursts will have different degrees of impact on the sun side navigation users; (2) the influence degree is positively correlated with the solar altitude angle; (3) the influence mode is similar to space-based suppression interference.

Timely and accurate monitoring of soil moisture in farmland is of great significance to the evaluation of crop growth and drought. Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing technology, can invert soil moisture based on the signal-to-noise ratio recorded by the measuring receiver. At present, existing studies tend to invert soil moisture in bare soil or low vegetation cover environments, and satellite selection depends on empirical values or prior information. Accordingly, a multisatellite combination method based on cross-correlation satellite selection for soil moisture inversion is proposed. Firstly, the trend and modulation terms in the signal-to-noise ratio of each satellite are effectively extracted by wavelet analysis. The characteristic of the wave term is analyzed, and the arc segment with an obvious and stable periodic oscillation is selected. Then, based on the interference phase of each satellite obtained by nonlinear least squares fitting, a cross-correlation satellite selection method (CCSSM) is established. The available satellites are selected by setting a reasonable threshold. Finally, three multi-satellite combination models for soil moisture inversion are constructed, and the inversion effects of each model are compared and analyzed. Taking the sugarcane planting area as an example, the results indicate that satellites can be screened quickly and effectively by CCSSM, and the selected satellites have strong cross-correlation. For short-term Global Navigation Satellite System (GNSS) observation data, it is more advantageous to use multiple linear regression model to invert soil moisture than machine learning.

One of the keys to retrieve soil moisture (SM) using the Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) technique is to correct for the influence of vegetation. In this paper, the surface reflectivity is calculated using the Cyclone Global Navigation Satellite System (CYGNSS) data, and combine the Vegetation Water Content (VWC) provided by Soil Moisture Active Passive (SMAP) data to establish linear regression model to retrieve SM products with a temporal resolution of 3 days and a spatial resolution of 36 km on a pan-tropical scale, and each of the models is parameterized pixel-by-pixel to allow for tuning in accordance with regional variations. According to the experimental findings, CYGNSS may offer useful SM estimations across regions with moderate vegetation, and the correlation coefficient (R) with SMAP reference data is up to 0.7. However, in the arid and densely vegetated regions, the retrieval performance is degraded, and the R is 0.4 and 0.3 in the forest and bare soil areas, respectively. The overall root mean square error (RMSE) is 0.042 cm3/cm3. In addition, a time-series comparison of in-situ data from the International Soil Moisture Network (ISMN) and the CYGNSS SM revealed a good correlation. The study proves the necessity of considering vegetation effect in SM retrieval, which is of positive significance for the promotion of the operational application of Spaceborne GNSS-R SM retrieval.

RTK with tilt compensation can achieve centimeter-level high-precision measurement. It belongs to contact measurement technology. However, in the case of signal limitation scenarios, the precision of contact measurement will be greatly reduced, or even unable to measure. This paper aims to analyze the potential and performance of a monocular camera combined with tilt compensation technology for non-contact measurement. According to the 3D model based on image reconstruction and the prior position and attitude of the GNSS receiver, the overall bundle adjustment is carried out to improve the attitude accuracy of the camera, thereby improving the accuracy of non-contact measurement. The error model of non-contact measurement is also derived in this paper, which has a very important guiding significance in practical engineering applications. The non-contact measurement method proposed in this paper does not need control points and can measure the absolute coordinates of target points in near real-time. The experimental results show that the method proposed in this paper can achieve centimeter-level high-precision measurement within the measurement range of 10m, and the mean values of horizontal, vertical, and 3D errors are 0.027 m, 0.018 m, and 0.032 m respectively.

Global Navigation Satellite System Reflectometry (GNSS-R) technology is gaining more and more attention from the scientific community due to its advantages of being all-weather, unaffected by clouds and rainfall, and low cost. The Cyclone Global Navigation Satellite System (CYGNSS), NASA’s first constellation of small satellites with space borne GNSS-R, was launched in late 2016, and CYGNSS data has now been shown to be useful for flood detection, in addition to the designed mission of inversion of sea surface wind fields. However, the quasi-random sampling of the surface by the CYGNSS constellation limits its potential for flood detection. Spatial interpolation techniques can bridge this gap and provide a complete coverage of high-resolution daily flood monitoring. In this paper we first introduce the CYGNSS surface reflectivity (SR) calculation method, secondly introduce a new spatial interpolation method (POBI) based on the interpolation of previously observed behavior and finally analyses the performance of CYGNSS high-resolution flood monitoring based on POBI using the 2022 Pakistan catastrophic floods as an example. The results show that compared with the common spatial interpolation methods, the CYGNSS observations based on the POBI method can not only obtain high-resolution flood monitoring results (daily, 3km), but also preserve the surface heterogeneity and discontinuity much better. The comparison with flood monitoring results obtained using microwave remote sensing data also demonstrates the feasibility of CYGNSS high spatial and temporal resolution flood monitoring based on POBI interpolation.

TomoSAR improves the information dimension of radar image from 2D to 3D, and is widely used in elevation modeling, deformation inversion of infrastructure such as high-rise buildings, dams and mining areas, and forest vegetation biomass estimation. However, the revisiting time of traditional spaceborne TomoSAR is more than 10 days, it is difficult to monitor and forecast the sudden deformation in real time. Furthermore, most of the low-orbit satellites are deployed in the north-south direction, so the deformation measurement accuracy in the north-south direction is relatively poor. The orbit baselines in height direction of low orbit satellites are small, so the image quantity and cost of high resolution tomography processing are huge. Therefore, low orbit TomoSAR is difficult to achieve high frequency, high precision and low-cost three-dimensional deformation measurement. Although GEO SAR and MEO SAR systems have the advantages of shorter revisit period, longer observation time and larger beam coverage area theoretically, there are no satellites in orbit at present. GNSS-TomoSAR, based on the reflected signals of navigation satellites, uses the in-orbit navigation satellite as the irradiation source, and a stationary receiver is placed on the ground to receive the echo signal reflected from the scene for synthetic aperture radar tomographic processing, which can achieve high frequency, high precision and low-cost three-dimensional deformation monitoring of the scene. GNSS-TomoSAR is a relatively novel signal processing concept, which is rarely studied at home and abroad. In this paper, the signal processing model of GNSS-TomoSAR is established, the performance boundary of GNSS-TomoSAR is discussed, and the actual track of Beidou IGSO satellite is used to conduct the simulation of height inversion of point target in GNSS-TomoSAR configuration. In above, the application prospect of GNSS-TomoSAR technology is preliminarily explored, lying a foundation for GNSS-TomoSAR real data processing.

In recent years, soil moisture retrieval using GNSS reflection signals has become a topic of great concern to scholars. However, most researchers adopt GPS and BDS signals to carry out the related research. There are insufficient studies on soil moisture inversion using QZSS (Quasi-Zenith Satellite System), and the performance of soil moisture inversion with different QZSS signals has not been evaluated yet. This study aims to provide new results by conducting a soil moisture monitoring experiment of QZSS-Reflectometry (QZSS-R) using the QZSS GEO satellite signal in the experimental field of Weihai Academy of Agricultural Sciences, Shandong Province. The original Intermediate Frequency (IF) data were collected, and the experimental data on October 21, 2022 (during bare soil period) were processed by using the self-developed GNSS-R software receiver. The results of different soil moisture inversion models were evaluated for L1 C/A and L5 signals, and it is found that the signal quality of L5 is better than that of L1 C/A in QZSS-R soil moisture inversion. For the Topp model, the RMSE (Root Mean Square Error) of L5 and L1 C/A inversion results are 0.0240 cm3/cm3 and 0.1725 cm3/cm3. For the Wang model, the RMSE of L5 and L1 C/A inversion results are 0.0317 cm3/cm3 and 0.1856 cm3/cm3 respectively. These results demonstrate that the soil moisture estimations accuracy of QZSS L5 signal are superior to that of L1 C/A.

The coexistence of multiple methods has become a general trend in tidal level monitoring, especially the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology and satellite altimetry technology provide a richer database for sea level monitoring. Few studies have carried out the comparison of tidal level accuracy obtained by the two methods. Based on this, this study takes BUR2 station as an example to carry out the research on tidal level monitoring of GNSS-IR and satellite altimetry (HY-2B and Jason-3), and compares them with the measured tidal level data provided by the tidal gauge station (BURTG). First, unify the respective time bases to Coordinated Universal Time (UTC) and the elevation base to Tide Gauge Zero (TGZ). Then, the characteristics of the two methods to monitor the tidal level are compared and analyzed. GNSS-IR technology has higher temporal resolution (R: 0.957, RMSE: 0.257 m, ME: −0.008 m, Number: 73936), while HY-2B (R: 0.994, RMSE: 0.105 m, ME: 0.025 m, Number: 178) and Jason-3 (R: 0.995, RMSE: 0.113 m, ME: −0.040 m, Number: 461) have higher inversion accuracy. This study explored the respective characteristics of GNSS-IR technology and satellite altimetry technology for tidal level monitoring, laying a foundation for the subsequent integration of tidal level monitoring.

The BDS-3 PPP-B2b signal provides the high-precision real-time precise point positioning (RTPPP) service for China and its surrounding areas, without relying on the internet communication to data reception. This technology can provide precise positioning services and Precipitable Water Vapor (PWV) retrieval services for open seas in eastern China, remote areas in Asian countries, as well as regions where communication system destroyed by disasters. For the limited studies exploring the BDS-3 PPP-B2b performance of signal service and application in PWV retrieval, this paper used one-month observation data from 8 IGS MGEX stations in China and neighboring countries to analyze the service capability of the PPP-B2b signal. The results show that the B2b orbit product’s precision in radial (R), along (A), and cross (C) directions are better than 0.08, 0.25, and 0.30 m, respectively. The standard deviation (STD) of clock offsets is better than 0.23 ns. The accuracy of static PPP solutions is at the centimeter level, while it is at the decimeter level for the dynamic PPP solutions. The central stations have better results than those at the boundary of the service areas. For exploring the application of the PPP-B2b signal in the retrieval application service of PWV, the zenith tropospheric delay (ZTD) is estimated based on the PPP-B2b signal, which is highly consistent with the results from the WUM analysis center products. The accuracy of the root mean square (RMS) is 3.41mm and the STD is 2.84 mm for PWV retrieval products based on PPP-B2b, which compared with the post-production. The RMS for the boundary station is less than 4 mm, meeting the accuracy requirements. The experiments indicate that the PPP-B2b signal can provide precise positioning services for the service areas, where lack of communication, and provide more reliable data for applications such as real-time numerical weather prediction.

The Beidou surface deformation measuring radar can realize the three-dimensional deformation measurement of the whole scene by using a single receiver. Since the system uses an in-orbit satellite as a transmitter, its long-term measurement costs are much lower than GBSAR and space-borne InSAR systems. However, due to the small power of navigation star to earth, the signal to noise ratio of scene echo is low. Based on the comparison between the high precision GBSAR one-dimensional deformation measurement data and the deformation measurement results of the Beidou surface deformation measurement radar repeater, this paper initially verifies the deformation measurement capability of the Beidou surface deformation measurement radar. This paper first introduces the Beidou surface deformation measuring radar, and then lists the PS processing flow of the Beidou surface deformation measuring radar according to the characteristics of the system. Finally, a multi-system joint verification experiment is carried out in Zhujiawan area of Chongqing, and a cross-system measurement accuracy verification method is proposed. Based on the three days’ measurement results, it is proved that GBSAR and Beidou surface deformation measurement radar have the same measurement results, and the three-dimensional deformation measurement results of the strong scattering point are consistent with the actual situation. The experiment proves that the Beidou surface deformation measuring radar has the ability to measure the three-dimensional deformation of the whole scene and can be widely used in disaster prediction.

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious.

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models.

Improving the accuracy of troposphere Zenith Total Delay (ZTD) is essential in Global Navigation Satellite System (GNSS). In this study, a high precision ZTD model for Chinese Southeast region is constructed by the ERA5 reanalysis data, considering the relationship between ZTD and elevation mainly shows a negative exponential form and the ZTD at a certain elevation has a significant periodicity. Therefore, combining the periodicity of ZTD at the Mean Sea Level (MSL) with the elevation normalization factor, the ZTD of test stations can be acquired by restoring the grid ZTD to the same elevation with test stations. The ZTD derived from the static PPP (Precise Point Positioning) solutions in 2022 of CMONOC (Crustal Movement Observation Network of China) was used as the reference value to measure the accuracy of the model, and the error RMS (Root Mean Square) of the test stations was in the range of 1.73 cm ~ 5.12 cm, and the MAE (Mean Absolute Error) was in the range of 1.40 cm ~ 4.33 cm. The total error RMS is 3.39 cm and the MAE is 2.53 cm. This model fully takes the variability of ZTD with elevation in different latitude and longitude regions into account, and can meet the troposphere delay correction needs of regional GNSS navigation and positioning.

The “BeiDou Navigation Satellite System Application Industry Development Index” system (abbreviation: “BDS Index”, BDI) constructs with 3 level and more than 70 indicators from four dimensions: Education/scientific research and development, Technical performance, Economy, Social benefit. The indicators could monitor and identify the health problems of BeiDou Navigation Satellite System Application Industry (abbreviation: BDS Industry). The experts of “BDS Industry Development Think Tank” (abbreviation: “BDTT”) hold meeting to analyze the health problems and make suggestions, try to find out the bottleneck and obstacles of the BDS industry, and then leverage the companies to work out the solutions fixing the problems. BDI system would track the results and evaluate the solutions. So that through such kind of methodology to form the BDI positive feedback closed-loop ecosystem to achieve the purpose of promoting the high-quality development of the BDS industry.

The integrated navigation system serves as the command-and-control center for the offshore towed streamer seismic exploration, and its positioning results of seismic sources and detector array are also the key to inversion of seabed geological structure using seismic data. In recent decades, China's marine geophysical exploration integrated navigation system has been reliant on imports, which is expensive and constrained in key technologies. Therefore, to break the technical blockade imposed by foreign geophysical manufacturers and safeguarding the country's energy security, it is of great strategic significance to independently carry out the research and development of the integrated navigation system for offshore streamer seismic exploration. This research presents critical algorithms for streamer positioning data processing in real-time, near-real-time, and post-processing scenarios, respectively. And the basic architecture and function modules of the integrated navigation system developed are given based on the requirement analysis. Furthermore, the system is tested and verified using both simulation and measured data. The results demonstrate that the shot prediction accuracy of the system is better than 0.5 m in the along line direction, and the streamer positioning accuracy with simulation data in real time, near real time and post processing are better than 6 m, 3 m and 2.2 m, respectively. Additionally, the streamer positioning results of the measured data are close to those of mainstream foreign commercial software, indicating that the system meets the operation requirements of offshore streamer seismic exploration. At present, the system has been formally installed and implemented on some geophysical exploration vessels, and has achieved good application results in the exploration in South China Sea and Bohai Sea.

Common mode error (CME), as a kind of non-tectonic motion, is one of the most important error sources in Global Navigation Satellite System (GNSS) coordinate time series. In order to obtain a precise and reliable velocity at a station, the influence of CME should be removed or decreased. In this paper, independent component analysis (ICA) method is applied to extract CMEs and analyze their influences on the coordinate time series based on 24 GNSS reference stations in North China during 2011–2020 period. The results indicate that, (1) After CMEs filtered out by the ICA, the average correlation coefficients between the residual time series decrease from previously 0.5–0.7 to −0.04 in east, north and up directions at stations, implying that the strong correlation between the residual time series at stations is significantly reduced by the ICA filtering. (2) The RMS of residual time series is effectively suppressed after the ICA filtering, with the RMS decreasing about 43.95%, 52.25% and 38.13% in east, north and up direction, respectively. (3) The averages of uncertainties of velocity estimations are decreasing from 0.30, 0.27 and 0.42 mm/yr to 0.12, 0.09 and 0.22 mm/yr in east, north and up direction before and after the ICA filtering, respectively, showing that the precision of the velocity estimation is improved about 60%, 69% and 49% in the three directions. Therefore, it is necessary to do the filtering among the GNSS coordinate time series to remove the effect of CME and the ICA is demonstrated to be an effective method to apply the filtering, so as to extract the precise tectonic motion in North China.

The long-term GNSS time series contain abundant geodynamic and geophysical information. These time series are inevitably subject to random or continuous missing data caused by environmental or human activities. However, many mathematic methods cannot be directly applied to analyse these time series when missing data arise. Though some efforts have been made to interpolate the random missing data, the long gaps still cannot be interpolated properly. This paper seeks to apply machine learning (ML) models in missing data interpolation. These ML models are trained by using 12 site-motion-related physical variables, including the Sun’s and Moon’s coordinates, temperature, atmospheric pressure, and hydrology. Then the missing data are generated from these trained ML models. This method is tested on seven GNSS stations, and the results show that the interpolation precision can averagely reach 5.5 mm for 2-year gaps, and 4.9 mm for 1-year gaps. This new missing data interpolation method shows good performance in restoring incomplete time series, which will be helpful for further analysis of GNSS time series.

Beidou is widely used in various industries and fields of economic and social development. It is deeply integrated with big data, the Internet of Things, artificial intelligence and other emerging technologies, promoting the birth of a new business form of “Beidou + micro specialty”, and supporting the economic and social digital transformation and improving quality and efficiency. Beidou + micro specialty enables learners to have certain academic and professional qualities and industry employability in this field through flexible and systematic training. The micro specialty has built universal technologies, protocols, standards and systems such as integrated Beidou high-performance antenna, PPP precise single-point positioning, chip and terminal embedded development, integrated navigation, Beidou high-precision satellite-based enhancement [1], Beidou + SoC implementation, Beidou precise time service, Beidou + 5G, Beidou + AI, communication gateway cluster, UAV + digital twin processing, space-time security + data encryption, massive space-time data platform + APP processing, and pseudosatellite development frontier, As well as the system of typical cases of Beidou major demonstration projects (Beidou Kinetic Energy Project, Beidou Precision Transportation and Logistics (including high-speed field application), Beidou Precision Scene Monitoring, Beidou Smart Ship, Beidou Precision Agricultural Machinery and Agricultural Internet of Things Project, Beidou Smart Environmental Protection, Beidou Precision Time Service Network, Beidou + Quantum Security Integration, Beidou Police/Civil (such as school bus, tourism guide) Project, Beidou UAV + emergency rescue, etc.). The use of micro specialty mode to achieve more in-depth and efficient interdisciplinary integration can also better enhance the depth and width of talent training, and provide useful reference for the training of compound talents.

With the successful networking of BeiDou Navigation Satellite System (BDS), BDS B1I/B2I/B3I signals enriched GNSS-IR data sources. GNSS-IR technology used to monitor soil moisture content (SMC) is constantly developing, but there are still the following problems in the current research. Firstly, the elevation angle range used for inversion is mostly determined based on experience, ignoring the problem that the amplitude of reflected signal varies greatly within a certain elevation angle range due to the influence of antenna gain, soil roughness, which affects the inversion Performance; Secondly, using single-star data, the inversion accuracy of linear regression model established by least squares algorithm is poor. Therefore, this research proposed a BDS multi-frequency SMC inversion method that takes into account the amplitude stability of reflected signal. Based on the sliding window method, a stationary signal window is obtained intelligently as the data source for subsequent inversion. On this basis, joint inversion of multi-frequency signals is carried out. In addition, RANSAC algorithm is used to estimate model parameters to reduce noise interference. The experiment shows that (1) Using the window with stable amplitude change as the elevation angle range of the experiment is useful to improve the retrieval accuracy of SMC. Compared with the retrieval results of the empirical elevation angle range of 5–30°, the correlation coefficient increases in the range of 5.2–34.9%, and the root mean square error decreases in the range of 19.2–52.9%; (2) B1I/B2I/B3I triple-frequency signal fusion can reflect the soil moisture information near the measurement station more comprehensively; (3) Compared with the least square linear regression algorithm, the SMC estimate effect of RANSAC algorithm is better. Finally, the SMC was inverted using the delayed phase fusion of the BDS triple-frequency signal. The correlation coefficient can reach 0.9850, the root mean square error is 0.0066 cm3/cm3, and the average absolute error is 0.0053 cm3/cm3. Compared with the traditional method, this method has significantly improved.

Beidou-3 system has been officially built and provides services to users all over the world. RDSS time service is a time service method provided by Beidou-3. In this paper, the basic principles of RDSS one-way time service and RDSS two-way time service are discussed in detail, the monitoring and evaluation methods of RDSS time service are deeply studied, and the measured data are compared and analyzed. The experimental results show that the service accuracy of Beidou-3 is better than the published index requirements, among which the RMS of one-way service accuracy of RDSS is better than 3 ns, and the RMS of two-way service accuracy of RDSS is better than 2 ns. Compared with Beidou-2 RDSS, Beidou-3 has improved and optimized the service accuracy of RDSS, and the beam switching in the process of RDSS service will cause the fluctuation of service results, which can reach tens of nanoseconds in one-way service and several nanoseconds in two-way service.

The radio occultation (RO) observation data provided by the second generation of The Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) were used to study and verify the method of determining the tropopause height based on atmospheric refractivity covariance transform. The principle of selecting the scale factor a in the atmospheric refractivity covariance transform is discussed based on the occultation events of GPS and GLONASS. It is found that the vertical variation of the refractivity in the lower troposphere region is smoother when a is taken as 30 km, and the prominent peaks of the refractivity profiles are more conducive to the determination of the tropopause. Comparing the atmospheric refractivity with the covariance-transformed atmospheric refractivity, it is found that the change of latter is more obvious and prominent, which can be used for the determination of the tropopause. According to the RO data in the low, middle and high latitudes, the tropopause heights obtained by the atmospheric refractivity covariance transform was compared with the cold point tropopause (CPT) and the temperature lapse rate tropopause (LRT) heights determined by the occultation temperature profile in the same occultation event, and the results show that there is a remarkable consistency between the three results at different latitudes, which means that the method used to determine the tropopause height after the covariance transformation of the atmospheric refractivity is feasible.

As BDS-3 satellite navigation system has been built and put into use for more than two years, the application of BeiDou satellite navigation system has gradually presented a prosperous trend. In order to further promote the marketization, industrialization and internationalization of BeiDou application, this paper conducts research on the application service system of BeiDou Navigation Satellite system. Through making a conceptual analysis of BDS scale application and public application service, this paper lays a theoretical foundation for BDS large-scale application. A definition of BDS application service system is demonstrated in the perspective of BDS users. The BDS application service system architecture is designed at the same time, which clarifies the function of each component. This paper also discusses the progress at this stage, and puts forward suggestions for further improvement.

The intelligent reflecting surface (IRS) is similar to an antenna array, which consists of multiple reflecting units. By controlling the phase shift of each reflecting unit, the direction of beam reflection is changed based on the analog beam fugacity theory, thus achieving the purpose of enhancing or constructing a channel. Therefore, IRS has great advantages in solving the channel estimation problem under non-line-of-sight (NLOS) conditions. For this characteristic, this paper proposes a localization method based on compressed sensing technology for the case where the relative position of IRS and base station is unknown, using NLOS path signals for second-order position estimation, first deriving rough localization parameters for IRS cascade channel estimation, and then conducting a joint phase configuration of IRS reflecting The phase configuration of the unit is jointly optimized, and then the second-order precise position estimation is performed. The results show that the proposed method is applicable to the NLOS positioning system with unknown IRS position, and due to the optimization capability of the IRS for the beam, the success rate of the positioning solution is improved by more than 20% and the positioning error is reduced by more than 40%.

Ultra-wideband (UWB) positioning technology has currently become a research hotspot for local small-scale positioning and navigation due to its advantages of high accuracy and wide bandwidth. The UWB positioning accuracy based on time of arrival (TOA) can theoretically reach centimeter level accuracy. However, due to the susceptibility of UWB signals to system time latency and non line of sight (NLOS) errors during propagation, it is difficult to achieve the expected positioning accuracy. Based on the method of broadcasting clock differencing approach in GNSS positioning, a UWB time synchronization station is established. Time differencing observation information is used to calibrate the time latency of the base station relative to the main base station. The unknown parameters include tag position information and equivalent time delay latency. The UWB/INS tightly coupled model combines Kalman filtering with ranging innovation vector to reduce the impact of UWB NLOS errors on positioning accuracy by multi-factor adaptive adjustment of the weight of UWB ranging information containing NLOS errors. The impact of time latency is also considered. The experimental results show that the static UWB positioning plane accuracy can reach centimeter level by considering the time latency. The tightly coupled UWB/INS positioning, considering both time latency and NLOS errors, can effectively reduce the influence of system time latency and NLOS errors on positioning accuracy. The positioning accuracy and stability are further improved compared with the conventional UWB/INS combination.

In recent years, X-ray pulsar-based navigation and timing have been widely concerned. Period searching is a key technique of pulsar navigation and pulsar timing. Since the pulsar signal is extremely weak, an accurate result of pulsar period requires a large amount of pulsar photons. However, the current method for pulsar period searching is of high computational cost when the amount of pulsar photons is very large. In this paper, we propose a Gaussian process (GP) surrogate model assisted multi-optimization algorithm to reduce the computational cost of period searching. In this algorithm, GP with low computational cost is used as a surrogate of the objective function of period searching. Besides, the proposed multi-optimization algorithm combines the advantages of Particle swarm optimization (PSO) and Cross-Entropy (CE) algorithm, which guarantees the accuracy of algorithm. The performance of the proposed algorithm is verified by the experiments with the simulation dada of PSR B1821-24 and Crab pulsar. Experiment results shows that the proposed method can efficiently reduce the computation cost while remains the accuracy of period searching. Besides, the proposed algorithm has good universality that can be used in different period searching method such as the epoch folding method and the MLE method.

At present, the accuracy and reliability of Global Navigation Satellite Systems (GNSS) in complex environments have not been able to meet the demands of seamless location services both indoors and outdoors. The 5G cellular network provides technical characteristics of ultra-dense networking, large bandwidth, and large-scale array antenna, which can offer wide-area and high-precision information on Time of Arrival (ToA) and Angle of Arrival (AoA). This paper proposes an adaptive integrated navigation algorithm based on a federated Kalman filter, integrating three sensors-GNSS/5G base station/Strap-down Inertial Navigation System (GNSS-5G-SINS) - to resolve the signal blocking issue in the indoor-outdoor transition environment. To overcome frequent out-of-lock and non-line-of-sight errors of the satellite in the indoor-outdoor seamless environment, this algorithm realizes the tight combination of observed values in the sub-filter and designs a fault detection and processing module to suppress the influence of abnormal observed values on the system. Finally, the main filter fuses information from each sub-filter and distributes dynamic information according to the quality of observed values. Experimental tests demonstrate that the proposed scheme improves the accuracy and stability of the navigation system. Under the partial lock-out of the satellite, the proposed algorithm reduces the positioning error by 68% compared with the GNSS-SINS compact combination, and the Root Mean Square Error (RMSE) reached 0.37m in the indoor environment. These results indicate that the new 5G technology has the potential to offer relatively high precision positioning services.

In recent years, USA, Russia and China have carried out many sea launch and recovery experiments, sea launch site exhibits better safety and economical efficiency compared with land launch site, which can provide launch service for spacecraft with different orbits. GNSS/INS integrated navigation system is adopted by rockets to acquire positioning results, nevertheless, owing to the inherit vulnerability of GNSS and possible INS failure, other navigation system is required to offer independent multi-source positioning data. TDOA positioning system is able to calculate rocket’s position utilizing time measurement data from telemetering signals, on one hand, the reliability can be promoted, and on the other hand, real-time central monitoring can be achieved, which aroused an increasing number of interests. However, present TDOA system usually apply land stations to obtain time measurements and localization results, because the characteristics of flat station distribution along the vertical direction, the vertical positioning precision cannot be satisfactory, especially for the need of rocket recovery on the sea launch site. To solve this problem, this work first discusses the precision influence of different surveying ships distribution layouts, “T” shape distribution is recommended for surveying ships according to evaluation results, then introduces air-based surveying station into TDOA positioning system to improve vertical localization accuracy, probes the performance variation utilizing different UAVs, including tethered UAV, multi-rotor UAV, medium-scale vertical takeoff and landing fixed wing UAV and large-scale fixed wing UAV, confirms the optimal UAV height in the rocket recovery key area under 10 km height, then researches on surveying station optimal distribution layout strategy according to predefined trajectory. This work can enhance the positioning precision and reliability, and provide three-dimensional surveying station distribution guidance in sea launch site.

With the development of millimeter wave technology and the increasingly complex electromagnetic environment, the traditional method of DOA estimation based on subspace technology can not meet the high-precision position requirements of space-time networks in various application scenarios. For this reason, scholars have introduced compressed sensing and sparse reconstruction technology into the problem of array signal estimation, and used sparse Bayesian (SBL) reconstruction based methods to estimate the angle of arrival (DOA), which often faces the problems of estimation performance and computation adjustment. In this paper, we propose a Bayesian grid iteration algorithm with low computational complexity while ensuring high estimation performance. Firstly, the SBL with the lowest computational complexity is used for rough estimation of the direction of arrival, and the grid is divided unevenly according to the rough estimation results. After the grid division termination condition is reached, the MDL criterion is used to estimate the number of sources, and the signal is decomposed into SVD. Finally, the updated grid division is combined with the SVD decomposition results, and the DOA is fine estimated using Root SBL. Simulation results show that this algorithm has low computational complexity and high estimation performance.

Global navigation satellite system/acoustic (GNSS/A) underwater positioning technique is widely used in the fields of marine scientific research and engineering applications. The conventional single-differenced (SD) positioning method generally treats the position of acoustic transducer obtained by GNSS positioning as known without error. However, error inevitably exists in the estimation of the transducer’s position determined by GNSS positioning, and the precision varies at different epochs. Ignoring the errors of acoustic transducer coordinates will lead to a worse estimation of the position of seafloor transponder. In this contribution, a joint adjustment method for precise GNSS/A underwater positioning is presented based on single-differenced observations. The positions of both transducer and transponder are treated as unknown parameters, and the positions of acoustic transducer are considered as virtual observations. The Helmert variance component estimation is used to adjust the weight ratio of two heterogeneous observations. To verify the performance of the proposed method, two field experiments were carried out. The lake experiment results show that the positioning accuracy with the proposed method can be improved by approximately 49% compared with the SD positioning method. The sea experiment results further demonstrate that the proposed method can perform much better than the SD positioning method, with the standard deviation values of coordinate components better than 0.06 m and the root mean square errors of the acoustic ranging residuals better than 0.02 m.

In recent years, it is of great significance to construct a high-precision positioning scheme for intelligent transportation in non-exposed space for all-airspace spatiotemporal network to obtain continuous and accurate position information of high-speed trains. Due to the narrow tunnel environment, when the train quickly passes through the positioning base station (BS), serious Doppler frequency shift mutation will occur, which reduces the tracking accuracy and robustness of the carrier ring and seriously affects the positioning accuracy of the train. Therefore, an adaptive tracking loop based on feedback local error search structure (ATLBOFLESS) is designed. The simulation results show that the proposed method can effectively reduce the influence of Doppler shift mutation on the tracking accuracy and robustness of the carrier loop.

Spoofing interference poses a threat to the positioning and timing security of GNSS that cannot be ignored. In traditional spoofing interference detection algorithms, the spoofing rate is often regarded as an unknown constant, which is not consistent with the actual spoofing interference. For the case of spoofing rate jitter in actual spoofing interference, this paper proposes an innovation variance spoofing interference detection algorithm based on reference noise. The jittered spoofing rate is modelled as a random variable obeying non-zero mean Gaussian distribution, and the detection statistics of innovation sequence variance is constructed by comparing the reference noise variance in the stationary no-spoofing state with the innovation sequence variance in the motion state for spoofing interference detection. Simulation results show that the proposed method has high sensitivity and good detection performance for spoofing of jitter rate. Compared with the traditional method, the proposed algorithm has obvious advantages in detection speed under real spoofing environment. In addition, the proposed method is insensitive to the mean of spoofing rate and has positive detection performance especially for slow-varying deception.

Shadow matching method can effectively improve the positioning accuracy of street crossing direction in urban canyons. This paper discusses the selection method of search area where the matching initial positioning point is not too accurate. Aiming at the problem of insufficient positioning along the street, this paper discusses the matching method of satellite signals with fuzzy direction range along the street. On the basis of clustering shadow matching, the user's position is finally determined through the weighted solution of different scores.

It is more and more significant to quickly solve GNSS (Global Navigation Satellite System) jamming problem whenever and wherever possible. The dynamic jammer is more difficult to be localized, tracked and deactivated than the stationary one, so usually poses a greater threat. Based on thought of GNSS + Network (such as 5G mobile communication, Internet and Internet of Things) enabling, a new approach proposed in this paper generates the localization result of a dynamic or stationary jammer in real-time by fusion of the basic observation information, position information and factory data from GNSS and mobile communication integrated terminals. Then it obtains the trail of the jammer near real-time by reprocessing multi-epoch localization results. The structure and estimation method of jammer localization error sources are analyzed in depth, and further localization and trajectory tracking of a GNSS jammer for multiple typical moving scenes is simulated in this paper. Finally, an optimization strategy of the critical parameter is presented for multiple or unknown jammer dynamics. It shows through simulation and analysis that, the approach presented is accurate and stable; the localizing and tracking accuracy is insensitive to position error of receivers, and is slightly affected by receiver density; under typical situations, tracking accuracy of a single stationary and dynamic jammer is up to 30m and 50m or so respectively.

In recent years, in the context of smart cities and the internet of everything, with the rapid development of the intelligent indoor environment, the demand for indoor location services in many industries has become higher and higher, and the need for real-time location of personnel has become more and more urgent. In this paper, a UWB-based circular antenna array single base station is designed for indoor space single base station 3D positioning problem, and the joint Time of Arrival (TOA)/Angle of Arrival (AOA) positioning estimation algorithm is studied. In terms of direction finding, a five-array element direction finding model is established using a uniform circular array, and the Phase Difference of Arrival (PDOA) algorithm is combined to obtain the signal arrival angle information, and TOA is used to complete the distance measurement between the base station and the label, to achieve the calculation of label location information. Also, for the consideration of improving the accuracy of angle measurement, the ambiguity resolution method of antenna array element phase difference for long baseline is proposed. Finally, the system performance was tested and verified in the experimental environments. The results show that the UWB single base station can be used to achieve indoor 3D positioning, its positioning accuracy is better than 1m. Furthermore, the technology can effectively solve problems such as the high deployment cost of multiple base stations, complicated system construction, and so on in practical applications with more excellent application and promotion value.

The Moon, as the only natural satellite of the Earth, is a crucial destination for human space exploration and serves as a foundation for deeper space exploration. Cis-lunar space offers vast opportunities for human expansion beyond the Earth’s land and ocean. The exploration, construction, and development of cis-lunar space demand enhanced cis-lunar space navigation. While GNSS navigation technology for the Earth and near-Earth space is well-established, extending it to cis-lunar space is the current stage. Small satellite technology development provides new ideas for efficient and rapid deployment of small satellite navigation constellations. To meet the cis-lunar space navigation demands, we propose a small space-based navigation system using BDS for time-transfer, based on Earth-Moon libration points orbits and small satellite platform.In this paper, we systematically study the multi-type orbital and navigation characteristics adopted, and design and analyze the architecture of the cis-lunar space small satellite navigation system using BDS timing. The proposed navigation constellation using multiple types of orbits, including DRO, NRHO, etc., is timed through BDS to compensate for the limited load of small satellites and the limited accuracy of the star clock. We analyze the observability of navigation constellations and evaluate the navigation performance of several constellation design cases using corresponding accuracy indicators. Our results verify the feasibility and preliminary performance of the proposed cis-lunar space small satellite navigation system using BDS as time-transfer, and provide a reference for BDS and GNSS to provide navigation services to cis-lunar space. We propose a new idea and method for cis-lunar space navigation that combines BDS navigation technology and small satellite platform, and which holds great promise for future space exploration.

Precise navigation is the key technology of civil aircraft flight safety. Considering the problems of low quality and poor interactivity of navigation data transmission of aircraft, this paper attempts to construct a CPS theoretical framework of “architecture hierarchy integration-model heterogeneous collaboration” of the navigation system. Combined with the characteristics of a multi-domain physics-information model, an architecture hierarchy integration, heterogeneous model exchange and data integration for system engineering is proposed. Results show that the simulation results are in good agreement with the measured data, and the guidance deviation is within the error range. This method can effectively analyze and process the mapping relationship between navigation data and aircraft spatial position information and realize the accurate identification of motion trajectory and prediction of action route of civil aircraft. It expands methods and tools for solving the problem of integrity monitoring of navigation systems and can provide data support for the establishment of air-borne navigation data standards and application specifications.

With the development of China’s lunar exploration project, China will implement manned lunar landing and lunar base construction mission. However, the existing ground-based communication and navigation (C&N) system will not be able to satisfy the needs of users and the accuracy of C&N services between the earth and the moon, as well as lunar surface. By constructing a C&N constellation at the Earth-Moon libration points, it improves the DOP of the navigation mission of Earth-Moon transfer space users. The C&N constellation support the formation of C&N capabilities in the Earth-Moon space and on the moon surface with a small number of satellites. In this paper, the C&N constellation scheme of the Earth-Moon libration point is adopted, which deploys the C&N satellites at the L2 ~ L5 libration points. Aiming at the link planning problem between satellites and users in the constellation, beam scheduling and topology planning strategies are adopted through heuristic algorithms, and theoretical simulation is carried out.

As a crucial PNT component, star-sensor-based celestial navigation and positioning has been widely used in satellites, ships, aircraft, and other kinds of carriers. The larger the field of view of the star sensor, the more star points can be captured. Existing field of view of star sensors is generally not more than 20° × 20°, which limits the number of observed star points and creates difficulties for system calibration. Additionally, it is difficult to capture sufficient star points under cloudy weather conditions. This study proposes a celestial navigation and positioning method based on super-large field-of-view star-sensors, which can simultaneously image sky-wide objects, extending the effective field of view of star-sensors to 180° × 360°. The number of observable stars increases from tens to hundreds, facilitating system calibration and improving system usability. This study explores the corresponding algorithms of fisheye star image acquisition, star point extraction, star identification, and astronomical attitude determination algorithms. It is experimentally verified that the astronomical longitude error obtained using this method is −2.4 arcsecond, the astronomical latitude error is 4.7 arcsecond, and the corresponding position error is 153.2 m. If a higher resolution CCD is used, celestial navigation positioning with an accuracy of 30–50 m can be achieved.

The fifth generation (5G) signals are immune to Global Navigation Satellite System (GNSS) spoofing attacks, so they have the capability to help GNSS user equipment (UE) detect hazardous spoofing attacks. The conventional innovation-based fault detection scheme can be extended to the EKF-based GNSS-5G hybrid positioning system for spoofing detection. However, the UE maneuver causes abnormal values of innovation, resulting in frequent false alarms. To solve this problem, this paper constructs a new type of test statistics by modifying the “innovation” in the conventional detection method. It exploits GNSS single point positioning (SPP) results to predict the current 5G measurements, which are then used to replace the EKF-based predicted measurements. The difference between the newly estimated 5G measurements and the received 5G measurements is employed to construct the new “innovation” for spoofing detection. Results show that the false alarm probability due to UE’s maneuver is significantly reduced.

Due to the inherent characteristics of the marine environment and seawater medium, users cannot effectively use satellite navigation systems for positioning, timing, navigation and communication in the underwater environment. The autonomous, continuous, unified and accurate underwater PNT information faces significant challenges compared with the surface and space environment. There are still several critical technical bottlenecks that need to be broken. This paper presents the comprehensive application prospect of the underwater PNT system. It summarizes and analyzes the application and technology development status of the Underwater PNT system at home and abroad. It also puts forward the basic construction idea of the underwater PNT system based on various navigation methods such as inertial, acoustic, matching and radio. It points out the critical technical bottlenecks that are difficult and urgent to be breakthrough in the front. It also provides a reference for building a more ubiquitous, integrated, intelligent, and comprehensive PNT system.

The BeiDou Navigation Satellite System (BDS) is a national strategic major space infrastructure built and operated independently in China [1]. It has created a new system of global satellite navigation, such as heterogeneous mixed constellation, multi service integration and high-precision satellite to ground inter satellite measurement. It has the outstanding characteristics of large span of technological innovation, high complexity of satellite to ground integration and fast iterative evolution speed. In order to speed up the iterative evolution of technical state, reduce the development risk and improve the efficiency cost ratio of the project, the digital twin concept is introduced into the construction process of BDS, and the digital practice of experimental verification is carried out in different stages. Combined with the technical system characteristics of BDS-1, BDS -2 and BDS -3, this paper comprehensively combs the development and evolution of digital system test verification, compares the tasks, resources and means of test verification in different stages, and summarizes the achievements of BDS in the establishment of digital test verification system, the construction of modeling and simulation mode, the construction of fusion test platform and the exploration of space test route The innovation of BDS has brought great benefits to solidify the technical status of BDS, guarantee the implementation of engineering construction, and support the upgrading and development of the system. Finally, according to the development requirements of the subsequent national integrated PNT system, the development assumption of the next generation BDS test and verification system is further planned.

Global Navigation Satellite system (GNSS) is initially designed to provide services to objects on the earth surfaces, or in the earth-orbits with altitude below 3000 km. However, previous researches show that GNSS receivers mounted on GEO satellites can receive GNSS signals and generate positioning and navigation results as well. Considering that in some harsh situations, such as bad observation geometry, weak signal acquisition, etc., the on-board GNSS receivers can only transfer pseudo-range data to GEO satellites, this paper proposed an autonomous navigation method in which the information from GNSS receiver, optical attitude sensors, and inter-satellite links are fused together: at first, the extended state equation and measurement equation of a GEO satellite with optical sensor’s errors are deduced; then, an extended Kalman filter is designed to evaluate and compensate optical sensor’s errors via constructed earth-centered vector bias; later, a sequential correction is carried out using inter-satellite link measurements; finally, the performance of proposed method is analyzed through Monte-Carlo simulation. It shows that the average distance and velocity evaluation errors of proposed method are around 0.2 km and 0.05 m/s individually; the performance can meet the requirements of survivability of a class of high-orbit, long-life and high-value satellites.

The Global Navigation Satellite System (GNSS) plays a crucial role in providing Positioning, Navigation, and Timing (PNT) services, but its signal is easily blocked indoors, which cannot meet the demand for high-precision positioning and timing in indoor scenarios. To address this issue, we proposed the Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) integrated signal, which can achieve sub-meter positioning accuracy and nan-meter level time synchronization accuracy while ensuring the Quality of Service (QoS) requirement and power budget of communication system. But the existing study did not consider the mutual interference between Device-to-Device (D2D) and MS-NOMA signals. Our major contributions are: Firstly, we derive the communication performance of cellular communication users (C-Users), the communication performance of D2D users (D-Users) and the ranging performance of positioning users (P-Users) based on the D2D signals multiplexing MS-NOMA signals uplink scenario. Secondly, we model a power allocation problem that considers QoS requirement and power constraints. Then, we propose a Particle Swarm Optimization-Based (PSO-Based) Power Allocation (PSOBPA) strategy which allocates the signal power of the C-Users in the MS-NOMA signal system and the signal power of D-Users. The numerical results show that our proposed PSOBPA strategy can improve the total system throughput by approximately 15% and the average ranging accuracy by around 4% compared to the average power allocation algorithm with the same bandwidth.

The pulsar navigation test satellite 01 (XPNAV-01) is the first space test platform dedicated to exploring the verification of X-ray pulsar navigation technology in China. It has collected a large amount of scientific observation data in orbit, which can be used for scientific research and timing navigation analysis. In this work, more than three years of observations of the Crab pulsar of XPNAV-01 are analyzed, and the pulse profiles of the Crab pulsar each orbit, day and year are obtained. The experimental goal of the satellite being able to accurately “see” the pulsar was achieved. The pulse profiles of four energy bands based on the observation data of XPNAV-01 are reported for the first time in China, and their variation trends with energy are in good agreement with the observation results abroad. A variety of parameters are used to comprehensively evaluate the change of the Crab pulsar’s pulse profile shape over time. It is found that the distribution of each parameter is relatively uniform, and the standard deviation of each parameter is small. The estimated single pulse arrival time accuracy is about 83 μs. All these results indicate that the X-ray detector independently developed in China has a good in-orbit operation status in its first four years.