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.

Compact and broadband circularly polarized ring antenna with wide beam-width for multiple global navigation satellite systems

A compact and broadband circularly polarized （CP） annular ring antenna with wide beam-width is proposed for multiple global navigation satellite systems （GNSS） in the L1 band. The annular ring is excited by two modified L-probes with quadrature phase difference. It has a 36.3% 10-dB return loss bandwidth and a 13% 3-dB axial ratio bandwidth, because of the orthogonal L-probes with 90° phase difference. The measured peak gain of the antenna is 3.9 dBic. It can detect the satellites at lower elevation as its half power beam-width （HPBW） is 113° in both the x-z and y-z planes, achieving a cross-polarization level of larger than 25 dB. Noticeably, the antenna achieves 89% size reduction compared with the conventional half wavelength patch antennas. It can be used in hand-held navigation devices of multiple GNSS such as COMPASS, Galileo, GPS and GLONASS.

The Performance Evaluation of the Integration of Inertial Navigation System and Global Navigation Satellite System with Analytic Constraints

The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.

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.

LEO Enhanced Global Navigation Satellite System(LeGNSS):progress,opportunities,and challenges

With the completion of Chinese BeiDou Navigation Satellite System(BDS),the world has begun to enjoy the Positioning,Navigation,and Timing(PNT)services of four Global Navigation Satellite Systems(GNSS).In order to improve the GNSS performance and expand its applications,Low Earth Orbit(LEO)Enhanced Global Navigation Satellite System(LeGNSS)is being vigorously advocated.Combined with high-,medium-,and low-earth orbit satellites,it can improve GNSS performance in terms of orbit determination,Precise Point Positioning(PPP)convergence time,etc.This paper comprehensively reviews the current status of LeGNSS,focusing on analyzing its advantages and challenges for precise orbit and clock determination,PPP convergence,earth rotation parameter estimation,and global ionosphere modeling.Thanks to the fast geometric change brought by LEO satellites,LeGNSS is expected to fundamentally solve the problem of the long convergence time of PPP without any augmentation.The convergence time can be shortened within 1 minute if appropriate LEO constellations are deployed.However,there are still some issues to overcome,such as the optimization of LEO constellation as well as the real time LEO precise orbit and clock determination.

Micro-Electro-Mechanical-Sensor Inertial Navigation System-Assisted Global Navigation Satellite System Receiver Acquisition Scheme and Performance Evaluation

When an aircraft moves under a low carrier-to-noise ratio （CNR） or at a high speed, increasing the sensitivity of global navigation satellite system （GNSS） receiver is a goal quite hard to achieve. A novel acquisition scheme assisted with micro-electro-mechanical-sensor （MEMS） inertial navigation system （INS） is presented to estimate the Doppler caused by user dynamics relative to each satellite ahead of time. Based on tightly coupled GNSS/INS estimation algorithm, MEMS INS Doppler error that can be achieved is first described. Then, by analyzing the mean acquisition time and signal detection probability, the MEMS INS-assisted acquisition capabilities in cold, warm and hot starts are quantitatively determined and compared with the standard GNSS acquisition capability. The simulations and comparisons have shown that： the acquisition time in cold start can be shortened by at least 23 s, the time in warm start can be shortened to i s and the acquisition capability is improved 95%, and the reaequisition time in hot start can be shortened by around 0.090 s and the capability can be enhanced 40%. The results demonstrate the validity of the novel method.

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.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

GNSS spoofing detection for single antenna receivers via CNR variation monitoring

In this paper,a method for spoofing detection based on the variation of the signal’s carrier-to-noise ratio(CNR)is proposed.This method leverages the directionality of the antenna to induce varying gain changes in the signals across different incident directions,resulting in distinct CNR variations for each signal.A model is developed to calculate the variation value of the signal CNR based on the antenna gain pattern.This model enables the differentiation of the variation values of the CNR for authentic satellite signals and spoofing signals,thereby facilitating spoofing detection.The proposed method is capable of detecting spoofing signals with power and CNR similar to those of authentic satellite signals.The accuracy of the signal CNR variation value calculation model and the effectiveness of the spoofing detection method are verified through a series of experiments.In addition,the proposed spoofing detection method works not only for a single spoofing source but also for distributed spoofing sources.

Pre-processing for time domain image formation in SS-BSAR system

A pre-processing procedure is designed for a space-surface bistatic synthetic aperture radar （SS-BSAR） system when a time domain image formation algorithm is employed. Three crucial technical issues relating to the procedure are fully discussed. Firstly, unlike image formation algorithms operating in the frequency domain, a time domain algorithm requires the accurate global navigation satellite system （GNSS） time and position. This paper proposes acquisition of this information using a time-and-spatial transfer with precise ephemeris and interpolation. Secondly, synchronization errors and compensation methods in SS-BSAR are analyzed. Finally, taking the non-ideal factors in the echo and the compatibility of image formation algorithms into account, a matched filter based on the minimum delay is constructed. Experimental result using real data suggest the pre-processing is functioning properly.

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.

Reference satellite selection method for GNSS high-precision relative positioning

Selecting the optimal reference satellite is an important component of high-precision relat/ve positioning because the reference satellite directly influences the strength of the normal equation. The reference satellite selection methods based on elevation and positional dilution of precision （PDOP） value were compared. Results show that all the above methods cannot select the optimal reference satellite. We introduce condition number of the design matrix in the reference satellite selection method to improve structure of the normal equation, because condition number can indicate the ill condition of the normal equation. The experimental results show that the new method can improve positioning accuracy and reliability in precise relative positioning.

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.

A fine acquisition algorithm based on fast three-time FRFT for dynamic and weak GNSS signals

As high-dynamics and weak-signal are of two primary concerns of navigation using Global Navigation Satellite System(GNSS)signals,an acquisition algorithm based on threetime fractional Fourier transform(FRFT)is presented to simplify the calculation effectively.Firstly,the correlation results similar to linear frequency modulated(LFM)signals are derived on the basis of the high dynamic GNSS signal model.Then,the principle of obtaining the optimum rotation angle is analyzed,which is measured by FRFT projection lengths with two selected rotation angles.Finally,Doppler shift,Doppler rate,and code phase are accurately estimated in a real-time and low signal to noise ratio(SNR)wireless communication system.The theoretical analysis and simulation results show that the fast FRFT algorithm can accurately estimate the high dynamic parameters by converting the traditional two-dimensional search process to only three times FRFT.While the acquisition performance is basically the same,the computational complexity and running time are greatly reduced,which is more conductive to practical application.

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.

Rapid development methodology of agricultural robot navigation system working in GNSS-denied environment

Robotic autonomous operating systems in global n40avigation satellite system(GNSS)-denied agricultural environments(green houses,feeding farms,and under canopy)have recently become a research hotspot.3D light detection and ranging(LiDAR)locates the robot depending on environment and has become a popular perception sensor to navigate agricultural robots.A rapid development methodology of a 3D LiDAR-based navigation system for agricultural robots is proposed in this study,which includes:(i)individual plant clustering and its location estimation method(improved Euclidean clustering algorithm);(ii)robot path planning and tracking control method(Lyapunov direct method);(iii)construction of a robot-LiDAR-plant unified virtual simulation environment(combination use of Gazebo and SolidWorks);and(vi)evaluating the accuracy of the navigation system(triple evaluation:virtual simulation test,physical simulation test,and field test).Applying the proposed methodology,a navigation system for a grape field operation robot has been developed.The virtual simulation test,physical simulation test with GNSS as ground truth,and field test with path tracer showed that the robot could travel along the planned path quickly and smoothly.The maximum and mean absolute errors of path tracking are 2.72 cm,1.02 cm;3.12 cm,1.31 cm,respectively,which meet the accuracy requirements of field operations,establishing the effectiveness of the proposed methodology.The proposed methodology has good scalability and can be implemented in a wide variety of field robot,which is promising to shorten the development cycle of agricultural robot navigation system working in GNSS-denied environment.

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Global Navigation Satellite System(GNSS)-based passive radar(GBPR)has been widely used in remote sensing applications.However,for moving target detection(MTD),the quadratic phase error(QPE)introduced by the non-cooperative target motion is usually difficult to be compensated,as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective.Consequently,the moving target in GBPR image is usually defocused,which aggravates the difficulty of target detection even further.In this paper,a spawning particle filter(SPF)is proposed for defocused MTD.Firstly,the measurement model and the likelihood ratio function(LRF)of the defocused point-like target image are deduced.Then,a spawning particle set is generated for subsequent target detection,with reference to traditional particles in particle filter(PF)as their parent.After that,based on the PF estimator,the SPF algorithm and its sequential Monte Carlo(SMC)implementation are proposed with a novel amplitude estimation method to decrease the target state dimension.Finally,the effectiveness of the proposed SPF is demonstrated by numerical simulations and pre-liminary experimental results,showing that the target range and Doppler can be estimated accurately.