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.

Anti-Jamming Algorithm Based on Spatial Blind Search for Global Navigation Satellite System Receiver

A novel subspace projection anti-jamming algorithm based on spatial blind search is proposed,which uses multiple single-constrained subspace projection parallel filters.If the direction of arrival(DOA)of a satellite signal is unknown,the traditional subspace projection anti-jamming algorithm cannot form the correct beam pointing.To overcome the problem of the traditional subspace projection algorithm,multiple single-constrained subspace projection parallel filters are used.Every single-constrained anti-jamming subspace projection algorithm obtains the optimal weight vector by searching the DOA of the satellite signal and uses the output of cross correlation as a decision criterion.Test results show that the algorithm can suppress the jamming effectively,and generate high gain toward the desired signal.The research provides a new idea for the engineering implementation of a multi-beam anti-jamming algorithm based on subspace projection.

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.

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.

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.

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.

GNSS spoofing detection based on uncultivated wolf pack algorithm

In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.

GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System, GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System, SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS/SINS/视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS/SINS/视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

基于高度角随机模型的GNSS外辐射源雷达定位算法

针对GNSS外辐射源雷达定位时不同卫星对定位的误差贡献不同的问题,提出基于高度角随机模型的定位算法,理论分析目标位置估计量的克拉美罗界和统计特性,并计算卫星位置误差和地面站位置误差对定位误差的影响。仿真结果表明:所提出算法对不同GNSS卫星的直射、反射路径的伪距差测量值误差进行了合理分配,定位性能达到了克拉美罗界,且不会因为选星方案的改变而大幅恶化。对地面站位置误差和卫星位置误差的分析表明:地面站位置的标准差小于10 cm、卫星位置的标准差小于1 km时对定位总误差的贡献可以忽略不计。

基于PSO与AFSA的GNSS整周模糊度种群融合优化算法

载波相位测量是实现全球导航卫星系统(Global navigation satellite system, GNSS)快速高精度定位的重要途径,而准确解算整周模糊度是其中的关键步骤之一.粒子群算法(Particle swarm optimization, PSO)收敛速度快但易陷入局部最优,人工鱼群算法(Artificial fish swarm algorithm, AFSA)全局优化性能好但收敛速度慢,因此融合两种算法的优点,提出一种GNSS整周模糊度种群融合优化算法(PSOAF).首先,通过载波相位双差方程求解整周模糊度的浮点解和对应的协方差矩阵.然后,采用反整数Cholesky算法对模糊度浮点解作降相关处理.其次,针对整数最小二乘估计的不足通过优化适应度函数来提高算法的收敛性和搜索性能.最后,通过PSOAF算法对整周模糊度进行解算.通过经典算例和试验研究表明:PSOAF算法可以更快地收敛于最优解,搜索效率也更为出色,解算的基线精度可以控制在10 mm以内,在短基线的实际情况下具有较高的应用价值.

GNSS拒止时基于并行CNN-BiLSTM回归和残差补偿的UAV导航误差校正方法

全球导航卫星系统(GNSS)拒止时,GNSS/惯性导航系统(INS)组合导航系统的性能严重下降,导致无人机集群导航误差快速发散.目前,利用神经网络预测位置与速度代替GNSS导航信息可校正无人机INS误差,但该方法仍存在定位误差较高且在轨迹突变时预测精度急剧下降的问题.因此,提出了一种基于卷积-双向长短时记忆网络联合残差补偿的位置与速度预测方法,用于提高位置与速度预测精度.首先,针对GNSS拒止后GNSS/INS组合导航系统定位误差较高的问题,提出卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的融合模型,该模型可建立惯性测量单元(IMU)动力学测量数据与GNSS导航信息之间的关系,实现较准确的位置和速度预测.其次,针对轨迹突变时预测效果急剧下降的问题,提出并行CNNBiLSTM回归架构,在预测位置与速度的同时,挖掘IMU动力学测量数据、预测值与预测残差之间的关系,预测并补偿预测残差,增强模型在轨迹突变时的预测精度.仿真结果表明,所提模型在预测准确性、有效性和稳定性方面都优于CNN-LSTM、LSTM网络模型.

月球导航星座轨道的地球GNSS可见性评估

随着月球探索进入一个新的时代,为确保未来月球任务的实时高精度定位,大幅提高登月航天器的自主性,且减少对地球基站的依赖,选取合适的轨道构建月球导航星座,并且实现月球导航星座与地球GNSS的通信链路同步尤为重要。为评估月球导航星座轨道中卫星接收GNSS信号的性能,首先对椭圆形月球冻结轨道(ELFO)、近直线晕轨道(NRHO)、顺行圆形轨道(PCO)和低月球轨道(LLO)4种轨道进行仿真。然后基于天线方向图等指标仿真了GNSS卫星信号,并依据主旁瓣波束宽度和载噪比等仿真结果评估了4种不同轨道卫星对GNSS的可见性。结果表明,NRHO和ELFO对GNSS星座有较好的可视效果,最高可见时间占比达99.57%,保障了绝大部分时间内能够稳定接收GNSS信号,有助于实现月球导航星座轨道卫星的轨道和时钟校正,并保证轨道的定位性能。

GNSS卫星轨道机动探测技术进展

针对轨道机动探测是各种空间任务的共性关键技术,是全球卫星导航系统(GNSS)精密定轨定位数据处理的首要工作,在卫星导航领域具有重要的研究意义,尤其是中国多星座构型的北斗卫星导航系统,轨道机动频繁,影响更为严重的现状,研究分析全球卫星导航系统(GNSS)卫星轨道机动探测技术的进展:概述轨道机动的概念、机动方式和机动时间;归纳整理目前GNSS卫星轨道机动探测中常用的技术方法,包括基于卫星轨道的探测方法、基于卫星广播星历的探测方法以及基于观测值的探测方法;总结各类卫星轨道机动探测方法的特点,并统计分析GNSS卫星的轨道机动频次与时段,指出随着GNSS卫星轨道机动探测方法的不断完善,实时及事后卫星轨道机动探测问题都得到了显著改善,促进了GNSS服务性能的进一步提升;最后,总结现有轨道机动探测方法的优势与局限性,展望GNSS轨道机动卫星数据处理中尚须进一步解决的重要问题。

卡斯卡迪亚慢滑移信息的GNSS时空探测

针对断层慢滑移时空分布信息探测困难的问题,基于全球卫星导航系统(GNSS)坐标序列,提出一种利用多通道奇异谱分析(MSSA)探测卡斯卡迪亚消减带慢滑移事件时空分布的方法:根据慢滑移分量的振荡特点确定窗口长度,采用时间迟滞矩阵对协方差阵进行增广,以提高对异常信息的识别能力;对振幅归一化获取空间响应,通过快速傅里叶变换分析慢滑移频谱特征。结果表明,卡斯卡迪亚消减带2007-01-05—2007-02-23和2008-04-22—2008-06-14发生了2次慢滑移事件,2次事件呈东南、西北区域的反向运动特征,且东南部响应程度明显高于西北地区;频谱特征显示,慢滑移信息表现为低频特征,且主要为随机游走噪声。研究结果可为板内地震前兆性信息探测提供参考。

GNSS浮标高程坐标时间序列噪声特性分析

本文对四种不同尺寸的全球卫星导航系统(GNSS)浮标(波浪骑士浮标、5 m浮标、2 m浮标、1 m浮标)进行了海面高观测数据分析,以揭示海洋环境下GNSS坐标时间序列的噪声特性。通过数据解算,确定了各类型浮标在观测精度方面的不确定性,并且标准差分析表明5 m浮标具有最高的观测稳定性,而波浪骑士浮标表现出最大的不确定性。进一步的噪声模型分析发现,5 m、2 m和1 m浮标的坐标时间序列最佳噪声模型为“白噪声+幂律噪声”,而波浪骑士浮标的最佳噪声模型为“白噪声+闪烁噪声”。为了提升数据质量,本研究还采用奇异谱分析算法对坐标时间序列进行了降噪处理。结果表明,虽然降噪后各浮标的最佳噪声模型没有发生变化,但噪声量级明显降低,5 m浮标、波浪骑士浮标、2 m浮标和1 m浮标的噪声量级分别下降了16.75%、39.2%、27.5%和33.35%。综合来看,大尺寸浮标由于其较高的稳定性,能够提供更高精度的海面高观测数据。同时,通过奇异谱分析降噪处理,可有效降低噪声量级,从而提高观测数据的质量。本研究结果为海洋GNSS观测提供了重要的技术参考。

低成本GNSS终端多普勒平滑伪距定位算法

随着低成本全球卫星定位(GNSS)终端硬件性能的不断提升,其在定位领域的应用也更加广泛,然而低成本GNSS终端在数据采集中常受观测环境的影响,使得载波相位观测值的高精度定位较为困难。伪距定位算法因其定位模型简单且实时性好的优势被广泛使用,因此本文利用多普勒平滑伪距方法,充分利用了多普勒观测值的作用,通过多普勒平滑伪距噪声来提升观测值的测量精度,利用多普勒平滑伪距模型对静态与多种动态环境下的平滑效果进行测试和分析。实验结果表明:在静态情况下,采用多普勒平滑伪距算法,伪距单点定位与伪距实时差分定位模型定位中平面精度较平滑之前分别提高了25%与31%;在动态环境下,有遮挡动态实验其精度提升30%以上,空旷情况下定位精度提升15%以上,快速运动过程中其提升能达到20%左右。表明通过多普勒平滑伪距算法可以显著提高低成本GNSS终端的定位精度。

改进BIC的GNSS站坐标时序有色噪声特性分析

为了有效解决传统的极大似然估计方法存在的未知数多导致结果不准确的问题,提出一种改进BIC的GNSS站坐标时序有色噪声特性分析方法:选取全球范围内100个基准站2008—2022年坐标时序及仿真的100组坐标时序,采用赤池信息准则(AIC)、贝叶斯信息准则(BIC)、改进的贝叶斯信息准则(BIC_tp)对不同有色噪声模型进行噪声特性分析,获取基准站坐标序列的最优噪声模型及速度参数;探讨BIC_tp对噪声模型及速度估计的影响。结果表明,基准站坐标序列噪声模型呈现出多样性特征,在北(N)和东(E)方向主要表现为闪烁噪声+白噪声(FNWN)模型,天顶(U)方向主要表现为幂律噪声+白噪声(PLWN)模型;相比于AIC和BIC噪声模型估计准则,BIC_tp方法对有色噪声模型估计具有较高的准确性,尤其是闪烁噪声+随机游走噪声(RW)+白噪声(FNRWWN)模型对全球卫星导航系统(GNSS)站速度不确定度的影响在N与E方向远大于U方向;正确获取模型参数估计的实际不确定度及改正噪声分量对于合理应用GNSS坐标时间序列数据具有重要意义。

基于GNSS水汽和地面假相当位温观测的短时强降水阈值预报方法研究

基于2019年和2020年6—7月GNSS水汽监测网大气可降水量(PWV)资料和并址气象站的地面雨量、温、压、湿等同步观测数据,利用临界成功指数(CSI)和命中率(POD)两个检验指标,探索建立了基于PWV、6小时水汽增量(PWV*)及假相当位温距平(θ_(se)*)的短时强降水阈值预报方法,并利用2021年6—7月降水样本对该预报方法进行检验,结果显示CSI和POD分别为0.167和0.593,其评分高于目前常规业务方法对短时强降水的客观预报评分,其中约48%的短时强降水发生在预警之后的24小时内,约78%发生于48小时内。研究区域内78.6%的短时强降水样本发生在连续15小时PWV*的累积值(∑PWV*)≥75 mm且连续24小时θ_(se)*累积值(∑θ_(se)*)≥30 K的条件下;PWV高值区叠加∑PWV*和∑θ_(se)*的大值区对梅雨期短时强降水以及暴雨发生区域有较好的指示性。

基于GNSS的前向散射雷达网目标穿越特性研究

前向散射雷达可通过捕获目标穿越基线时引起的信号扰动实现目标检测,在隐身飞机目标探测方面具有一定潜力,目标穿越特性的研究对信号检测和参数估计具有重要意义。首先构建了基于全球导航卫星系统(global navigation satellite system,GNSS)的前向散射雷达网模型,分析了构成前向散射观测的条件和探测范围。然后,基于前向散射动态信号模型,提出采用时频域统计特征描述目标穿越基线的信号特性。最后,通过仿真分析了目标穿越位置和运动参数对散射信号及其统计特征的影响,揭示了前向散射信号的特征变化规律,对后续提高穿越事件检测概率和参数估计精度具有参考价值。

顾及河流流向的GNSS-R流速反演

针对传统的河流流速测量方法存在价格昂贵、维护困难或不准确等问题,提出一种顾及河流流向的GNSS-R流速反演方法:指出河流流向和反射几何构型会对GNSS-R流速反演造成影响,并利用GNSS信号相对反射面运动产生的多普勒效应进行流速测量;然后引入声学多普勒流速剖面仪(ADCP)实测数据对反演结果进行验证,分析不同几何构型下,散射效应对于多普勒频移的影响,并完善GNSS-R河流流速反演方法。实验结果表明,后向构型时,反演的河流流速值一般大于前向构型;基于后向构型的多普勒频率差对河流流速进行反演的结果与实测流速值变化趋势相近,但由于多普勒频率难以准确捕获的原因,反演中误差较大。