Satellite Navigation Method Based on High-Speed Frequency Hopping Signal

Global navigation satellite system has been widely used,but it is vulnerable to jamming.In military satellite communications,frequency hopping(FH)signal is usually used for anti-jamming communications.If the FH signal can be used in satellite navigation,the anti-jamming ability of satellite navigation can be improved.Although a recently proposed timefrequency matrix ranging method(TFMR)can use FH signals to realize pseudorange measurement,it cannot transmit navigation messages using the ranging signal which is crucial for satellite navigation.In this article,we propose dual-tone binary frequency shift keyingbased TFMR(DBFSK-TFMR).DBFSK-TFMR designs an extended time-frequency matrix(ETFM)and its generation algorithm,which can use the frequency differences in different dual-tone signals in ETFM to modulate data and eliminate the negative impact of data modulation on pseudorange measurement.Using ETFM,DBFSK-TFMR not only realizes the navigation message transmission but also ensures the precision and unambiguous measurement range of pseudorange measurement.DBFSK-TFMR can be used as an integrated solution for anti-jamming communication and navigation based on FH signals.Simulation results show that DBFSK-TFMR has almost the same ranging performance as TFMR.

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.

Evaluation of global navigation satellite system spoofing efficacy

The spoofing capability of Global Navigation Satellite System(GNSS)represents an important confrontational capability for navigation security,and the success of planned missions may depend on the effective evaluation of spoofing capability.However,current evaluation systems face challenges arising from the irrationality of previous weighting methods,inapplicability of the conventional multi-attribute decision-making method and uncertainty existing in evaluation.To solve these difficulties,considering the validity of the obtained results,an evaluation method based on the game aggregated weight model and a joint approach involving the grey relational analysis and technique for order preference by similarity to an ideal solution(GRA-TOPSIS)are firstly proposed to determine the optimal scheme.Static and dynamic evaluation results under different schemes are then obtained via a fuzzy comprehensive assessment and an improved dynamic game method,to prioritize the deceptive efficacy of the equipment accurately and make pointed improvement for its core performance.The use of judging indicators,including Spearman rank correlation coefficient and so on,combined with obtained evaluation results,demonstrates the superiority of the proposed method and the optimal scheme by the horizontal comparison of different methods and vertical comparison of evaluation results.Finally,the results of field measurements and simulation tests show that the proposed method can better overcome the difficulties of existing methods and realize the effective evaluation.

BeiDou Regional Navigation Satellite System Completed

As the 16th BeiDou navigation satellite was successfully launched into space at 23:33 Beijing Time from the Xichang Satellite Launch Center (XSLC) on October 25, 2012, China completed the construction of the BeiDou Regional Navigation Satellite System that starts to officially provide services for most parts of the Asia-Pacific region from December 27. The 16th BeiDou navigation satellite, the last one for the regional BeiDou system, was developed by China Academy of Space Technology under CASC.

China Launches the 11th BeiDou Satellite in Navigation Satellite System

China launched the 11th BeiDou navigation satellite in their BeiDou Navigation Satellite System.The satellite was launched from the Xichang Satellite Launch Center in Sichuan Province on a LM-3C rocket at 0:12 on February 25 (Beijing time) and was put into the predetermined transfer orbit successfully.The geostationary satellite is the first BeiDou navigation satellite launched in 2012 for the

Networking of BeiDou Navigation Satellite System Accelerated with Two New Members

At 4:50 on April 30, China's LM-3B/I rocket, an improved type based on LM-3B, made its debut at the Xichang Satellite Launch Center and successfully sending the 12th and 13th BeiDou Navigation Satellite System satellites into the planned transfer orbit in space. It was the first time that China launched two BeiDou satellites with one rocket. It was

Energetic electron detection packages on board Chinese navigation satellites in MEO

Energetic electron measurements and spacecraft charging are of great significance for theoretical research in space physics and space weather applications.In this paper,the energetic electron detection package(EEDP)deployed on three Chinese navigation satellites in medium Earth orbit(MEO)is reviewed.The instrument was developed by the space science payload team led by Peking University.The EEDP includes a pinhole medium-energy electron spectrometer(MES),a high-energy electron detector(HED)based onΔE-E telescope technology,and a deep dielectric charging monitor(DDCM).The MES measures the energy spectra of 50−600 keV electrons from nine directions with a 180°×30°field of view(FOV).The HED measures the energy spectrum of 0.5−3.0 MeV electrons from one direction with a 30°cone-angle FOV.The ground test and calibration results indicate that these three sensors exhibit excellent performance.Preliminary observations show that the electron spectra measured by the MES and HED are in good agreement with the results from the magnetic electron-ion spectrometer(MagEIS)of the Van Allen Probes spacecraft,with an average relative deviation of 27.3%for the energy spectra.The charging currents and voltages measured by the DDCM during storms are consistent with the highenergy electron observations of the HED,demonstrating the effectiveness of the DDCM.The observations of the EEDP on board the three MEO satellites can provide important support for theoretical research on the radiation belts and the applications related to space weather.

Scene image recognition with knowledge transfer for drone navigation

In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

Technological developments create a lot of impacts in the tourism industry.Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security.However,there is a difference between technological advances and their integration into the methods of tourism study.The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues.This study addresses the gap by using a synthesized application(app)for demographic surveys and Global Navigation Satellite System(GNSS)technology to implement research processes.This article focuses on managing visitors within the famous Freycinet National Park.Extremely comprehensive structured data were analyzed in three phases,(1)identifying groups of visitors who are more likely to use the walking trails,(2)those who are more and less likely to visit during/peak crowding times,and(3)finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time activity.This research examines innovative techniques that can offer energy resources to managers and tourism agencies,especially in detecting,measuring,and potentially relieving crowding and over-tourism.

The 10th BeiDou 2 Navigation Satellite Flew into Space

LM launch vehicles established a new record by successfully performing the 16th successful flight this year.A LM-3A launched the 10th BeiDou 2 satellite into its predetermined transfer orbit on December 2 from the XSLC in Sichuan Province.

Collecting volunteered geographic information from the Global Navigation Satellite System(GNSS):experiences from the CAMALIOT project

Raw observations(carrier-phase and code observations)from the Global Navigation Satellite System(GNSS)can now be accessed from Android mobile phones(Version 7.0 onwards).This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications.This paper presents results from data collection campaigns using the CAMALIOT mobile app.In the frst campaign,116.3 billion measurements from 11,828 mobile devices were collected from all continents.Although participation decreased during the second campaign,data are still being collected globally.In this contribution,we demonstrate the potential of volunteered geographic information(VGl)from mobile phones to fill data gaps in geodetic station networks that collect GNSS data,e.g.in Brazil,but also how the data can provide a denser set of observations than current networks in countries across Europe.We also show that mobile phones capable of dual-frequency reception,which is an emerging technology that can provide a richer source of GNSS data,are contributing in a substantial way.Finally,we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography,where the dominant motivation for participation is to contribute to scientific research.

A Dual-Satellite GNSS Positioning Algorithm of High Accuracy in Incomplete Condition

Global navigation satellite system could provide accurate positioning results in signal complete condition. However, the performance is severe when signal denied, especially for the single-mode Bei Dou receiver. This paper proposes a dual-satellite positioning algorithm to promote the positioning performance in the satellite signal gap. The new algorithm utilizes the previous positioning data stored in complete condition to simplify the positioning equations. As the clock bias persists for a short period, this proposed method could work out accurate positioning results by only two visible satellites, without the need of computing the clock bias. Also, the Kalman filtering algorithm is used to smooth the trajectories, and improve the positioning results. During the incomplete period, only two satellites for 30 seconds and three satellites for 60 seconds, the preliminary experiment result shows that, the presented method could provide almost the same positioning results as in complete condition.

Navigation jamming signal recognition based on long short-term memory neural networks

This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN).

Study on GNSS satellite signal simulator

Satellite signal simulator for global navigation satellite system(GNSS)can evaluate the accuracy of capturing,tracing and positioning of GNSS receiver.It has significant use-value in the military and civil fields.The system adopts the overall design scheme of digital signal processor(DSP)and field-programmable gate array(FPGA).It consists of four modules:industrial control computer simulation software,mid-frequency signal generator,digital-to-analog(D/A)module and radio frequency(RF)module.In this paper,we test the dynamic performance of simulator using the dynamic scenes testing method,and the signal generated by the designed simulator is primarily validated.

REVISITING THE DOPPLER FILTER OF LEO SATELLITE GNSS RECEIVERS FOR PRECISE VELOCITY ESTIMATION

The theoretical aspects of the precise velocity determination of Low Earth Orbit (LEO) satellites'on board Global Navigation Satellite Systems (GNSS) receivers are derived. It shows that the receiver's Phase Lock Loop (PLL) is required to feature extremely small group delay within its low frequency band, which is in contrast to existing work that proposed wide band linear phase filters. Following this theory, a Finite Impulse Response (FIR) filter is proposed. To corroborate, the proposed FIR filter and an Infinite Impulse Response (IIR) filter lately proposed in literals are implemented in a LEO satellite onboard GNSS receiver. Tests are conducted using a third party commercial GPS signal generator. The results show that the GNSS receiver with the proposed FIR achieves 11 mm/s R.M.S precision, while the GNSS receiver with the IIR filter has a filter-caused velocity error that can not be ignored for space borne GNSS receivers.

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

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

Modeling and Prediction of Inter-System Bias for GPS/BDS-2/BDS-3 Combined Precision Point Positioning

The combination of Precision Point Positioning(PPP)with Multi-Global Navigation Satellite System(MultiGNSS),called MGPPP,can improve the positioning precision and shorten the convergence time more effectively than the combination of PPP with only the BeiDou Navigation Satellite System(BDS).However,the Inter-System Bias(ISB)measurement of Multi-GNSS,including the time system offset,the coordinate system difference,and the inter-system hardware delay bias,must be considered for Multi-GNSS data fusion processing.The detected ISB can be well modeled and predicted by using a quadratic model(QM),an autoregressive integrated moving average model(ARIMA),as well as the sliding window strategy(SW).In this study,the experimental results indicate that there is no apparent difference in the ISB between BDS-2 and BDS-3 observations if B1I/B3I signals are used.However,an obvious difference in ISB can be found between BDS-2 and BDS-3 observations if B1I/B3I and B1C/B2a signals are used.Meanwhile,the precision of the Predicted ISB(PISB)on the next day of all stations is about 0.1−0.6 ns.Besides,to effectively utilize the PISB,a new strategy for predicting the PISB for MGPPP is proposed.In the proposed strategy,the PISB is used by adding two virtual observation equations,and an adaptive factor is adopted to balance the contribution of the Observed ISB(OISB)and the PISB to the final estimations of ISB.To validate the effectiveness of the proposed method,some experimental schemes are designed and tested under different satellite availability conditions.The results indicate that in open sky environment,the selective utilization of the PISB achieves almost the same positioning precision of MGPPP as the direct utilization of the PISB,but the convergence time of MGPPP is reduced by 7.1%at most in the north(N),east(E),and up(U)components.In the blocked sky environment,the selective utilization of the PISB contributes to more significant improvement of the positioning precision and convergence time than that in the open sky environment.Compared with the direct utilization of the PISB,the selective utilization of the PISB improves the positioning precision and convergence time by 6.7%and 12.7%at most in the N,E,and U components,respectively.

GPS观测得的2023年2月土耳其双震前后的地壳形变

2023年2月6日土耳其发生7.8级双震,相隔仅9小时、相距近,都是近直立断层走滑型地震,是利用GPS观测研究地震前后地壳形变稀有震例。利用美国网站得到131个GPS站欧亚(板块)参考框架位移数据,分析了2次地震前后的地壳形变。同震水平位移是分析大地震前兆地壳运动的关键现象。GPS分别观测得的2次地震同震位移,特征显著:水平位移最大达4.4 m;向西、向东和向南水平位移3个分区明显,与区域内3个板块构造运动密切相关;而垂直位移较小,无明显分区。2次地震前震中附近的水平位移分别出现(第一次地震)峰值和(第二次地震)闭锁2种类型,但无明显的垂直位移积累。与其他震例一样,在震前两种类型的水平位移区域,同震水平位移是震前位移在时间或空间上的(弹性)回跳,表明大地震有前兆形变。2次地震重合的孕震区内,有对2次地震同震水平位移影响相近的区域。GPS观测明确显示,地壳水平运动是2次大地震的成因。尽管观测不足和未能预报2次地震,但仍突显GNSS可在地震监测、预警和地震预测中发挥作用的优势。

Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

Temperature and pressure play key roles in Global Navigation Satellite System(GNSS) precipitable water vapor(PWV) retrieval. The National Aeronautics and Space Administration(NASA) and European Center for Medium-Range Weather Forecasts(ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2(MERRA-2) and the fifthgeneration ECMWF reanalysis(ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values.Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are-0.07 hPa and 0.45 K, with the root mean square error(RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are-0.01 hPa and 0.38 K, with the RMSE of 1.08 h Pa and 2.66 K, respectively.The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south,north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2(GNSS MERRA-2 PWV) and ERA5(GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.

Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.