基于GNSS信噪比数据的测站环境误差处理方法及其应用研究
发布时间：2018-07-15 13:13
【摘要】：全球卫星导航定位系统(Global Navigation Satellite System, GNSS)以其全天候、高精度、自动化、高效益等显著优点广泛应用于大地测量、地球动力学、地震及地质灾害、变形监测等学科研究及工程应用领域,极大地推动了测绘技术的发展及其应用。随着GNSS技术应用的日益广泛和深入,对高精度GNSS测量的需求日益增加,GNSS测量的各种误差处理也越来越受到重视。GNSS信号从发射、传播到接收,从来源上受到三类误差源的影响：与卫星有关的误差、与信号传播有关的误差以及与接收机有关的误差。与卫星和接收机硬件有关的误差包括钟差、天线相位偏差、轨道误差、硬件延迟、观测噪声等；信号传播过程中的误差包括空间环境(电离层、对流层等)传播误差和地面测站环境(多路径反射、地表植被、天线积雪、水汽及火山烟流等)传播误差。对于电离层、对流层、卫星钟差等大多数GNSS定位误差都可以通过建立系统误差模型、参数估计或差分技术予以消除或减弱。而对于多路径和天线积雪等地面测站环境误差,由于其复杂的局域性特征和较弱的空间相关性,目前还没有一种有效的改正模型或普遍适用的数据处理方法。地面测站环境的变化会引起信号强度、极化特性、传播方向和路径的改变,因而引起定位误差。例如测地型GNSS接收机载波相位多路径的影响约为厘米级,而在水面观测环境下的伪距多路径误差的最大影响可达7米,接收机天线积雪对精密单点定位PPP平面和高程方向的影响达数个厘米甚至更大。这些地面测站环境所引起的误差足以直接影响诸如精密导航定位、精密形变监测、结构振动监测以及地震同震分析的精度与可靠性,因而成为制约高精度定位及其应用的主要误差来源和关键因素。在高采样率(1Hz以上)的GNSS地震同震分析应用中,由于多路径误差与地震频率重叠且难以模型化,因而影响了地震分析的定位精度。在世界的许多地区,应用于全球性地球物理学研究的GNSS连续运行站点不可避免地受到冰雪等恶劣环境的影响,并导致显著的位置误差。如何有效地提取或消除多路径等测站观测环境误差的影响,是近年来国际上的一个研究热点。GNSS接收机在提供伪距、载波相位等主要观测值的同时,也提供衡量接收信号质量的信噪比(Signal-to-noise Ratio, SNR)数据。信噪比是表征GNSS接收信号质量的重要指标,其本身包含观测质量信息,同时对测站观测环境具有敏感性,信噪比数值的大小及其变化与测站周围的环境因素如季节、温度、土壤湿度、积雪等密切相关。由于GNSS信噪比数据在观测质量评定、平差随机模型构建以及测站环境误差处理等方面具有独特的利用价值,所以,对GNSS信噪比数据的研究与应用正日益受到关注。已有研究结果表明,在GNSS精密定位中视为GNSS信号噪声的多路径误差,包含了关于测站周围环境的有用信息。GNSS反射信号与多路径反射环境密切相关,土壤湿度、植被及积雪覆盖等测站环境会引起地面反射体特性的变化,并在天线接收的多路径反射信号中得到有效的反映,引起信噪比频率、幅度、相位等特征参量的变化,从而通过这些SNR特征参量建立多路径反射信号与土壤湿度等环境参数之间的映射关系,实现对于环境参数的反演。积雪、土壤及植被水分是陆地水循环中缺一不可的重要组成部分,对于整个气候和生态系统有着重要影响,其动态变化与环境和气候密不可分。近年基于多路径效应发展起来的GNSS遥感技术为土壤湿度、积雪厚度及植被生长等提供了一种全新的、高效率的监测手段。该技术充分利用已有的GNSS连续运行站网,进一步拓展了GNSS研究及应用领域,并与其他手段形成互补和验证,具有广阔的应用前景。本文对测站环境误差模型与时空特征、SNR数据与测站观测环境的关系及其响应等问题进行了深入分析,在此基础上,研究了基于GNSS信噪比数据的测站环境误差探测、提取及其改正方法,并探讨了SNR观测值在土壤湿度反演中的应用。主要研究工作及成果如下：1.系统、深入地探讨了GNSS多路径环境误差产生的物理机制、几何模型及其时空特性。结合仿真及实测GNSS数据,对观测值域和坐标域内的多路径环境误差的时频特征进行了研究。结果表明：多路径环境误差具有时域重复特征,重复周期约为236s；多路径环境误差有一定的幅度范围,伪距多路径误差一般不超过1个码元宽度,而相位多路径误差不超过1/4载波波长；多路径环境误差在频域上还具有一定的能量集中分布区间。这些时频特征为利用数字信号处理方法分离和提取测站环境误差提供了理论依据。此外,详细探讨了多路径反射环境下的信噪比模型及其特征,并分析了土壤湿度、地面植被、积雪及温度变化等测站环境因素对信噪比观测值的影响。信噪比观测值与测站环境的相关性为利用SNR数据处理环境误差和反演测站环境参数提供了理论依据。2.研究和探讨了GNSS信噪比观测值与载波相位多路径误差之间的关系,实现了基于信噪比数据的载波相位多路径误差改正算法,并利用实测数据对LC无电离层组合观测值残差进行了分析。结果表明：利用SNR能在一定程度上修正载波相位观测值中的多路径误差,提高定位解算的精度。LC观测值残差的能量主要集中于0.0001-0.0005Hz的低频区间,与多路径环境误差的特征频率区间较为一致。3.提出了基于SNR观测值探测冰雪引起GNSS站点坐标序列异常值的算法,根据SNR值识别位置时间序列中的相应粗差并进行算法改正。利用美国大陆板块边界观测网(Plate boundary observation, PBO) GPS站点的观测数据对该算法进行了验证和分析。异常探测算法改正结果表明：在站点线性构造运动的假设下,坐标序列最佳线性拟合的RMS值从改正前的0.29cm、0.16cm和1.67cm (E、 N、V)减小到0.12cm、 0.11cm和0.44cm,算法改正后的站点位置估算值精度得到了有效改善。4.研究了GNSS信噪比多径干涉反演土壤湿度的函数描述模型,并对信噪比数据质量及其选取策略、有效反演区域的确定等问题进行了深入探讨。在此基础上,设计和实现了基于Matlab平台的GNSS土壤湿度反演程序包GNSS_SMI.结合实例进行了基于GNSS多径相位的土壤湿度反演,利用模拟仿真及现场实测土壤湿度数据对反演结果进行了对比和分析,并对多径相位与土壤湿度的相关关系进行了量化描述。研究表明：土壤湿度的有效反演区域是一组与天线高、卫星高度角和方位角相关的椭圆,选择L2波段高级卫星并符合多路径反射模型的SNR数据更有利于湿度反演；SNR相位φ为土壤湿度反演和监测土壤湿度变化趋势提供了重要的感应指标,指数函数能够较好地描述延迟相位与土壤含水率之间的映射关系。5.顾及到季节、天气、植被、坡度等因素的短时变化对SNR相位参数的影响较小,提出一种基于滑动时间窗口的土壤湿度估算方法。分别利用全时段数据、滑动时间窗口预测以及滑动时间窗口插值等3种方法反演了土壤湿度并对反演结果进行了对比和精度分析。这3种方法的相关系数平均值分别为0.717、0.832和0.952。与全时段方法相比较,基于窗口的插值和预测方法分别上升了16.2%和32.9%,误差L1范数分别下降了39.8%和62.0%,误差L2范数下降了17.4%和54.6%。结果表明：相比于全部时段观测量构建模型的土壤湿度反演方法,利用时间窗口建模反演土壤湿度能有效地模拟短时间内不变的测站反射环境,从而改善反演精度；基于窗口的插值方法能将误差降低至理想的效果,但无法实现土壤湿度的近实时预计；基于窗口的预测方法获取的精度略低于插值结果,但其能用于近实时的应用。
[Abstract]:Global Navigation Satellite System (GNSS) is widely used in geodetic survey, geodynamics, earthquake and geological disaster, deformation monitoring and other fields of engineering and engineering, which has greatly promoted the development and application of Surveying and mapping technology for its all-weather, high precision, automation, high efficiency and so on. With the wider and deeper application of GNSS technology, the demand for high precision GNSS measurement is increasing. The various error processing of GNSS measurement has also been paid more and more attention to the influence of three types of error sources on the source of.GNSS signals from the source: error related to the satellite, error related to the signal propagation and reception Error related to machines. Errors related to satellite and receiver hardware include clock difference, antenna phase deviation, orbit error, hardware delay, observation noise, and so on; errors in the process of signal propagation include space environment (ionosphere, troposphere, etc.) propagation error and ground station ring boundary (multipath reflection, surface vegetation, antenna snow, water vapor and fire) For the ionosphere, the troposphere and the satellite clock difference, most of the GNSS positioning errors can be eliminated or weakened by establishing the system error model, the parameter estimation or the difference technique. For the multi path and the antenna snow, the environmental errors of the ground stations are due to their complex local characteristics and weak spatial correlation. There is no effective correction model or widely applicable data processing method at present. The change of ground station environment will cause the change of signal intensity, polarization, propagation direction and path, thus causing location error. For example, the influence of the carrier phase path of the geodesic GNSS receiver is about centimeter, and in the water surface observation environment The maximum effect of the pseudo distance multipath error is up to 7 meters, and the effect of the receiver antenna snow on the plane and elevation direction of the precise single point positioning PPP is several centimeters or even larger. The errors caused by these ground station environments are sufficient to directly affect the positioning of precision navigation, precision change monitoring, structural vibration monitoring and seismic iseismic points. The accuracy and reliability of analysis have become the main source of error and key factors to restrict high precision positioning and its application. In the application of GNSS seismic analysis with high sampling rate (above 1Hz), the location accuracy of seismic analysis is influenced by the overlapping of the multipath error and the seismic frequency and the accuracy of the seismic analysis. In many areas of the world, GNSS continuous operating stations applied to global geophysical research are inevitably affected by the harsh environment such as ice and snow, and lead to significant positional errors. How to effectively extract or eliminate the influence of environmental errors in multi path observation stations is a hot research hotspot in the world in recent years to provide pseudo range and load. The signal to noise ratio (Signal-to-noise Ratio, SNR) is also provided to measure the quality of the received signal. The signal-to-noise ratio is an important indicator of the quality of the GNSS receiving signal. It contains the observation quality information, and is sensitive to the observation environment of the station, the size of the signal to noise ratio and the change of the station week. Environmental factors such as season, temperature, soil moisture, snow and so on are closely related. Because the GNSS signal-to-noise ratio data have unique utilization value in observation quality assessment, construction of random model and Station Environmental error processing, the research and application of GNSS signal to noise ratio data are becoming more and more concerned. In GNSS precision positioning, it is considered as a multipath error of GNSS signal noise, which contains useful information about the surrounding environment of the station, which is closely related to the multi path reflection environment. Soil moisture, vegetation and snow cover will cause the change of the ground reflector specificity, and the multipath reflection letter received by the antenna. The signal is effectively reflected, causing the change of the characteristic parameters such as the frequency, amplitude and phase of the signal to noise ratio. Through these SNR characteristic parameters, the mapping relation between the multi path reflection signal and the soil moisture and other environmental parameters is established to realize the inversion of the environmental parameters. The snow, the soil and the vegetation moisture are indispensable in the land water cycle. Important components have important influence on the whole climate and ecosystem, and their dynamic changes are closely related to the environment and climate. In recent years, the GNSS remote sensing technology based on the multipath effect has provided a new and efficient monitoring method for soil moisture, snow thickness and vegetation growth. GNSS continuous station network has further expanded the field of GNSS research and application, and forms complementary and verification with other means. It has a broad application prospect. In this paper, the relationship between the measuring station environment error model and space-time characteristics, the relationship between the SNR data and the observation environment of the station and the response and so on are deeply analyzed. Based on this, the base is studied. The method of detection, extraction and correction of station environment error of GNSS signal to noise ratio data, and the application of SNR observation value in soil moisture inversion are discussed. The main research work and results are as follows: 1. system, the physical mechanism of GNSS multipath environmental error, the model and its temporal and spatial characteristics, combined with the simulation and measured GNSS The time frequency characteristics of multi-path environmental errors in the observed and coordinate domains are studied. The results show that the multi path environment error has the time domain repetition feature and the repetition period is about 236s; the multipath environment error has a certain range, and the pseudo range multipath error is not more than 1 bit width, and the phase multipath error is incorrect. The difference is not more than the 1/4 carrier wavelength, and the multipath environmental error also has a certain energy concentration range in the frequency domain. These time frequency features provide a theoretical basis for the separation and extraction of Station Environmental errors by using digital signal processing methods. In addition, the signal to noise ratio model and its characteristics under the multi path reflection ring are discussed in detail, and the analysis is also analyzed. The influence of environmental factors such as soil moisture, ground vegetation, snow and temperature change on the signal-to-noise ratio observation value. The correlation between the signal to noise ratio observation value and the station environment provides a theoretical basis for using SNR data to deal with environmental errors and to inverse the environmental parameters of the station. The multi path of the GNSS signal to noise ratio observation value and the carrier phase multipath is discussed. The relationship between the error and the carrier phase multipath error correction algorithm based on the SNR data is realized, and the measured data are used to analyze the residual error of the LC ionospheric combined observation. The results show that the multipath error in the carrier phase observation value can be corrected by SNR to a certain extent, and the accuracy.LC view of the positioning solution is improved. The energy of the measured residual is mainly concentrated in the low frequency range of 0.0001-0.0005Hz, and the characteristic frequency interval of the multi-path environmental error is more consistent..3. proposes an algorithm based on the SNR observation value to detect the abnormal value of the coordinate sequence of the GNSS site, which is based on the SNR value to identify the corresponding rough difference in the position time sequence and make the algorithm correction. The algorithm is verified and analyzed by the observational data of the Plate boundary observation (PBO) GPS site. The correction results of the anomaly detection algorithm show that the RMS value of the best linear fitting of the coordinate sequence is reduced to 0 from the corrected 0.29cm, 0.16cm and 1.67cm (E, N, V) before the correction of the linear tectonic movement of the site. .12cm, 0.11cm and 0.44cm, the accuracy of the location estimation of the site after the algorithm corrections can effectively improve the function of.4. to study the function description model of the GNSS signal to noise ratio multipath interference inversion of soil moisture, and discuss the quality of the signal to noise ratio data and the selection strategy and the determination of the effective inversion area. In this paper, a GNSS soil moisture inversion program based on Matlab platform is presented in this paper, which combines the soil moisture inversion based on the GNSS multipath phase. The results of the inversion are compared and analyzed using the simulated simulation and the field measured soil moisture data, and the correlation between the multipath phase and the soil moisture is quantitatively described. It is shown that the effective inversion area of soil moisture is a group of ellipses with high antenna, high angle and azimuth of the satellite. The selection of the L2 band advanced satellite and the SNR data that conforms to the multi path reflection model are more beneficial to the humidity inversion, and the SNR phase provides an important induction index for the soil moisture inversion and the monitoring of soil moisture change trend. The exponential function can describe the mapping relationship between the delay phase and the soil moisture content well..5. takes into account the short time changes of the factors such as season, weather, vegetation, slope and other factors on the SNR phase parameters. A method of estimating soil moisture based on the sliding time window is proposed. The sliding time window is used to predict the time window respectively. 3 methods, such as the sliding time window interpolation and other methods, are used to inverse the soil moisture and to compare and analyze the results of the inversion. The mean values of the correlation coefficients of the 3 methods are 0.717,0.832 and 0.952., respectively, and the window based interpolation and prediction methods are up 16.2% and 32.9% respectively, and the error L1 norm drops respectively. 39.8% and 62%, the error L2 norm decreased by 17.4% and 54.6%. results show: compared to the soil moisture inversion method of the whole time view measurement construction model, using the time window modeling inversion of soil moisture can effectively simulate the short time invariable station reflection environment, thus improving the inversion accuracy; the window based interpolation method can be used The error is reduced to the ideal effect, but it can not realize the near real-time prediction of soil moisture; the accuracy of the window based prediction method is slightly lower than the interpolation result, but it can be used in the near real-time application.
【学位授予单位】：中国地质大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：P228.4
[Abstract]:Global Navigation Satellite System (GNSS) is widely used in geodetic survey, geodynamics, earthquake and geological disaster, deformation monitoring and other fields of engineering and engineering, which has greatly promoted the development and application of Surveying and mapping technology for its all-weather, high precision, automation, high efficiency and so on. With the wider and deeper application of GNSS technology, the demand for high precision GNSS measurement is increasing. The various error processing of GNSS measurement has also been paid more and more attention to the influence of three types of error sources on the source of.GNSS signals from the source: error related to the satellite, error related to the signal propagation and reception Error related to machines. Errors related to satellite and receiver hardware include clock difference, antenna phase deviation, orbit error, hardware delay, observation noise, and so on; errors in the process of signal propagation include space environment (ionosphere, troposphere, etc.) propagation error and ground station ring boundary (multipath reflection, surface vegetation, antenna snow, water vapor and fire) For the ionosphere, the troposphere and the satellite clock difference, most of the GNSS positioning errors can be eliminated or weakened by establishing the system error model, the parameter estimation or the difference technique. For the multi path and the antenna snow, the environmental errors of the ground stations are due to their complex local characteristics and weak spatial correlation. There is no effective correction model or widely applicable data processing method at present. The change of ground station environment will cause the change of signal intensity, polarization, propagation direction and path, thus causing location error. For example, the influence of the carrier phase path of the geodesic GNSS receiver is about centimeter, and in the water surface observation environment The maximum effect of the pseudo distance multipath error is up to 7 meters, and the effect of the receiver antenna snow on the plane and elevation direction of the precise single point positioning PPP is several centimeters or even larger. The errors caused by these ground station environments are sufficient to directly affect the positioning of precision navigation, precision change monitoring, structural vibration monitoring and seismic iseismic points. The accuracy and reliability of analysis have become the main source of error and key factors to restrict high precision positioning and its application. In the application of GNSS seismic analysis with high sampling rate (above 1Hz), the location accuracy of seismic analysis is influenced by the overlapping of the multipath error and the seismic frequency and the accuracy of the seismic analysis. In many areas of the world, GNSS continuous operating stations applied to global geophysical research are inevitably affected by the harsh environment such as ice and snow, and lead to significant positional errors. How to effectively extract or eliminate the influence of environmental errors in multi path observation stations is a hot research hotspot in the world in recent years to provide pseudo range and load. The signal to noise ratio (Signal-to-noise Ratio, SNR) is also provided to measure the quality of the received signal. The signal-to-noise ratio is an important indicator of the quality of the GNSS receiving signal. It contains the observation quality information, and is sensitive to the observation environment of the station, the size of the signal to noise ratio and the change of the station week. Environmental factors such as season, temperature, soil moisture, snow and so on are closely related. Because the GNSS signal-to-noise ratio data have unique utilization value in observation quality assessment, construction of random model and Station Environmental error processing, the research and application of GNSS signal to noise ratio data are becoming more and more concerned. In GNSS precision positioning, it is considered as a multipath error of GNSS signal noise, which contains useful information about the surrounding environment of the station, which is closely related to the multi path reflection environment. Soil moisture, vegetation and snow cover will cause the change of the ground reflector specificity, and the multipath reflection letter received by the antenna. The signal is effectively reflected, causing the change of the characteristic parameters such as the frequency, amplitude and phase of the signal to noise ratio. Through these SNR characteristic parameters, the mapping relation between the multi path reflection signal and the soil moisture and other environmental parameters is established to realize the inversion of the environmental parameters. The snow, the soil and the vegetation moisture are indispensable in the land water cycle. Important components have important influence on the whole climate and ecosystem, and their dynamic changes are closely related to the environment and climate. In recent years, the GNSS remote sensing technology based on the multipath effect has provided a new and efficient monitoring method for soil moisture, snow thickness and vegetation growth. GNSS continuous station network has further expanded the field of GNSS research and application, and forms complementary and verification with other means. It has a broad application prospect. In this paper, the relationship between the measuring station environment error model and space-time characteristics, the relationship between the SNR data and the observation environment of the station and the response and so on are deeply analyzed. Based on this, the base is studied. The method of detection, extraction and correction of station environment error of GNSS signal to noise ratio data, and the application of SNR observation value in soil moisture inversion are discussed. The main research work and results are as follows: 1. system, the physical mechanism of GNSS multipath environmental error, the model and its temporal and spatial characteristics, combined with the simulation and measured GNSS The time frequency characteristics of multi-path environmental errors in the observed and coordinate domains are studied. The results show that the multi path environment error has the time domain repetition feature and the repetition period is about 236s; the multipath environment error has a certain range, and the pseudo range multipath error is not more than 1 bit width, and the phase multipath error is incorrect. The difference is not more than the 1/4 carrier wavelength, and the multipath environmental error also has a certain energy concentration range in the frequency domain. These time frequency features provide a theoretical basis for the separation and extraction of Station Environmental errors by using digital signal processing methods. In addition, the signal to noise ratio model and its characteristics under the multi path reflection ring are discussed in detail, and the analysis is also analyzed. The influence of environmental factors such as soil moisture, ground vegetation, snow and temperature change on the signal-to-noise ratio observation value. The correlation between the signal to noise ratio observation value and the station environment provides a theoretical basis for using SNR data to deal with environmental errors and to inverse the environmental parameters of the station. The multi path of the GNSS signal to noise ratio observation value and the carrier phase multipath is discussed. The relationship between the error and the carrier phase multipath error correction algorithm based on the SNR data is realized, and the measured data are used to analyze the residual error of the LC ionospheric combined observation. The results show that the multipath error in the carrier phase observation value can be corrected by SNR to a certain extent, and the accuracy.LC view of the positioning solution is improved. The energy of the measured residual is mainly concentrated in the low frequency range of 0.0001-0.0005Hz, and the characteristic frequency interval of the multi-path environmental error is more consistent..3. proposes an algorithm based on the SNR observation value to detect the abnormal value of the coordinate sequence of the GNSS site, which is based on the SNR value to identify the corresponding rough difference in the position time sequence and make the algorithm correction. The algorithm is verified and analyzed by the observational data of the Plate boundary observation (PBO) GPS site. The correction results of the anomaly detection algorithm show that the RMS value of the best linear fitting of the coordinate sequence is reduced to 0 from the corrected 0.29cm, 0.16cm and 1.67cm (E, N, V) before the correction of the linear tectonic movement of the site. .12cm, 0.11cm and 0.44cm, the accuracy of the location estimation of the site after the algorithm corrections can effectively improve the function of.4. to study the function description model of the GNSS signal to noise ratio multipath interference inversion of soil moisture, and discuss the quality of the signal to noise ratio data and the selection strategy and the determination of the effective inversion area. In this paper, a GNSS soil moisture inversion program based on Matlab platform is presented in this paper, which combines the soil moisture inversion based on the GNSS multipath phase. The results of the inversion are compared and analyzed using the simulated simulation and the field measured soil moisture data, and the correlation between the multipath phase and the soil moisture is quantitatively described. It is shown that the effective inversion area of soil moisture is a group of ellipses with high antenna, high angle and azimuth of the satellite. The selection of the L2 band advanced satellite and the SNR data that conforms to the multi path reflection model are more beneficial to the humidity inversion, and the SNR phase provides an important induction index for the soil moisture inversion and the monitoring of soil moisture change trend. The exponential function can describe the mapping relationship between the delay phase and the soil moisture content well..5. takes into account the short time changes of the factors such as season, weather, vegetation, slope and other factors on the SNR phase parameters. A method of estimating soil moisture based on the sliding time window is proposed. The sliding time window is used to predict the time window respectively. 3 methods, such as the sliding time window interpolation and other methods, are used to inverse the soil moisture and to compare and analyze the results of the inversion. The mean values of the correlation coefficients of the 3 methods are 0.717,0.832 and 0.952., respectively, and the window based interpolation and prediction methods are up 16.2% and 32.9% respectively, and the error L1 norm drops respectively. 39.8% and 62%, the error L2 norm decreased by 17.4% and 54.6%. results show: compared to the soil moisture inversion method of the whole time view measurement construction model, using the time window modeling inversion of soil moisture can effectively simulate the short time invariable station reflection environment, thus improving the inversion accuracy; the window based interpolation method can be used The error is reduced to the ideal effect, but it can not realize the near real-time prediction of soil moisture; the accuracy of the window based prediction method is slightly lower than the interpolation result, but it can be used in the near real-time application.
【学位授予单位】：中国地质大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：P228.4
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