【摘要】：全球衛(wèi)星導(dǎo)航定位系統(tǒng)(Global Navigation Satellite System, GNSS)以其全天候、高精度、自動(dòng)化、高效益等顯著優(yōu)點(diǎn)廣泛應(yīng)用于大地測(cè)量、地球動(dòng)力學(xué)、地震及地質(zhì)災(zāi)害、變形監(jiān)測(cè)等學(xué)科研究及工程應(yīng)用領(lǐng)域,極大地推動(dòng)了測(cè)繪技術(shù)的發(fā)展及其應(yīng)用。隨著GNSS技術(shù)應(yīng)用的日益廣泛和深入,對(duì)高精度GNSS測(cè)量的需求日益增加,GNSS測(cè)量的各種誤差處理也越來(lái)越受到重視。GNSS信號(hào)從發(fā)射、傳播到接收,從來(lái)源上受到三類誤差源的影響：與衛(wèi)星有關(guān)的誤差、與信號(hào)傳播有關(guān)的誤差以及與接收機(jī)有關(guān)的誤差。與衛(wèi)星和接收機(jī)硬件有關(guān)的誤差包括鐘差、天線相位偏差、軌道誤差、硬件延遲、觀測(cè)噪聲等；信號(hào)傳播過(guò)程中的誤差包括空間環(huán)境(電離層、對(duì)流層等)傳播誤差和地面測(cè)站環(huán)境(多路徑反射、地表植被、天線積雪、水汽及火山煙流等)傳播誤差。對(duì)于電離層、對(duì)流層、衛(wèi)星鐘差等大多數(shù)GNSS定位誤差都可以通過(guò)建立系統(tǒng)誤差模型、參數(shù)估計(jì)或差分技術(shù)予以消除或減弱。而對(duì)于多路徑和天線積雪等地面測(cè)站環(huán)境誤差,由于其復(fù)雜的局域性特征和較弱的空間相關(guān)性,目前還沒(méi)有一種有效的改正模型或普遍適用的數(shù)據(jù)處理方法。地面測(cè)站環(huán)境的變化會(huì)引起信號(hào)強(qiáng)度、極化特性、傳播方向和路徑的改變,因而引起定位誤差。例如測(cè)地型GNSS接收機(jī)載波相位多路徑的影響約為厘米級(jí),而在水面觀測(cè)環(huán)境下的偽距多路徑誤差的最大影響可達(dá)7米,接收機(jī)天線積雪對(duì)精密單點(diǎn)定位PPP平面和高程方向的影響達(dá)數(shù)個(gè)厘米甚至更大。這些地面測(cè)站環(huán)境所引起的誤差足以直接影響諸如精密導(dǎo)航定位、精密形變監(jiān)測(cè)、結(jié)構(gòu)振動(dòng)監(jiān)測(cè)以及地震同震分析的精度與可靠性,因而成為制約高精度定位及其應(yīng)用的主要誤差來(lái)源和關(guān)鍵因素。在高采樣率(1Hz以上)的GNSS地震同震分析應(yīng)用中,由于多路徑誤差與地震頻率重疊且難以模型化,因而影響了地震分析的定位精度。在世界的許多地區(qū),應(yīng)用于全球性地球物理學(xué)研究的GNSS連續(xù)運(yùn)行站點(diǎn)不可避免地受到冰雪等惡劣環(huán)境的影響,并導(dǎo)致顯著的位置誤差。如何有效地提取或消除多路徑等測(cè)站觀測(cè)環(huán)境誤差的影響,是近年來(lái)國(guó)際上的一個(gè)研究熱點(diǎn)。GNSS接收機(jī)在提供偽距、載波相位等主要觀測(cè)值的同時(shí),也提供衡量接收信號(hào)質(zhì)量的信噪比(Signal-to-noise Ratio, SNR)數(shù)據(jù)。信噪比是表征GNSS接收信號(hào)質(zhì)量的重要指標(biāo),其本身包含觀測(cè)質(zhì)量信息,同時(shí)對(duì)測(cè)站觀測(cè)環(huán)境具有敏感性,信噪比數(shù)值的大小及其變化與測(cè)站周圍的環(huán)境因素如季節(jié)、溫度、土壤濕度、積雪等密切相關(guān)。由于GNSS信噪比數(shù)據(jù)在觀測(cè)質(zhì)量評(píng)定、平差隨機(jī)模型構(gòu)建以及測(cè)站環(huán)境誤差處理等方面具有獨(dú)特的利用價(jià)值,所以,對(duì)GNSS信噪比數(shù)據(jù)的研究與應(yīng)用正日益受到關(guān)注。已有研究結(jié)果表明,在GNSS精密定位中視為GNSS信號(hào)噪聲的多路徑誤差,包含了關(guān)于測(cè)站周圍環(huán)境的有用信息。GNSS反射信號(hào)與多路徑反射環(huán)境密切相關(guān),土壤濕度、植被及積雪覆蓋等測(cè)站環(huán)境會(huì)引起地面反射體特性的變化,并在天線接收的多路徑反射信號(hào)中得到有效的反映,引起信噪比頻率、幅度、相位等特征參量的變化,從而通過(guò)這些SNR特征參量建立多路徑反射信號(hào)與土壤濕度等環(huán)境參數(shù)之間的映射關(guān)系,實(shí)現(xiàn)對(duì)于環(huán)境參數(shù)的反演。積雪、土壤及植被水分是陸地水循環(huán)中缺一不可的重要組成部分,對(duì)于整個(gè)氣候和生態(tài)系統(tǒng)有著重要影響,其動(dòng)態(tài)變化與環(huán)境和氣候密不可分。近年基于多路徑效應(yīng)發(fā)展起來(lái)的GNSS遙感技術(shù)為土壤濕度、積雪厚度及植被生長(zhǎng)等提供了一種全新的、高效率的監(jiān)測(cè)手段。該技術(shù)充分利用已有的GNSS連續(xù)運(yùn)行站網(wǎng),進(jìn)一步拓展了GNSS研究及應(yīng)用領(lǐng)域,并與其他手段形成互補(bǔ)和驗(yàn)證,具有廣闊的應(yīng)用前景。本文對(duì)測(cè)站環(huán)境誤差模型與時(shí)空特征、SNR數(shù)據(jù)與測(cè)站觀測(cè)環(huán)境的關(guān)系及其響應(yīng)等問(wèn)題進(jìn)行了深入分析,在此基礎(chǔ)上,研究了基于GNSS信噪比數(shù)據(jù)的測(cè)站環(huán)境誤差探測(cè)、提取及其改正方法,并探討了SNR觀測(cè)值在土壤濕度反演中的應(yīng)用。主要研究工作及成果如下：1.系統(tǒng)、深入地探討了GNSS多路徑環(huán)境誤差產(chǎn)生的物理機(jī)制、幾何模型及其時(shí)空特性。結(jié)合仿真及實(shí)測(cè)GNSS數(shù)據(jù),對(duì)觀測(cè)值域和坐標(biāo)域內(nèi)的多路徑環(huán)境誤差的時(shí)頻特征進(jìn)行了研究。結(jié)果表明：多路徑環(huán)境誤差具有時(shí)域重復(fù)特征,重復(fù)周期約為236s；多路徑環(huán)境誤差有一定的幅度范圍,偽距多路徑誤差一般不超過(guò)1個(gè)碼元寬度,而相位多路徑誤差不超過(guò)1/4載波波長(zhǎng)；多路徑環(huán)境誤差在頻域上還具有一定的能量集中分布區(qū)間。這些時(shí)頻特征為利用數(shù)字信號(hào)處理方法分離和提取測(cè)站環(huán)境誤差提供了理論依據(jù)。此外,詳細(xì)探討了多路徑反射環(huán)境下的信噪比模型及其特征,并分析了土壤濕度、地面植被、積雪及溫度變化等測(cè)站環(huán)境因素對(duì)信噪比觀測(cè)值的影響。信噪比觀測(cè)值與測(cè)站環(huán)境的相關(guān)性為利用SNR數(shù)據(jù)處理環(huán)境誤差和反演測(cè)站環(huán)境參數(shù)提供了理論依據(jù)。2.研究和探討了GNSS信噪比觀測(cè)值與載波相位多路徑誤差之間的關(guān)系,實(shí)現(xiàn)了基于信噪比數(shù)據(jù)的載波相位多路徑誤差改正算法,并利用實(shí)測(cè)數(shù)據(jù)對(duì)LC無(wú)電離層組合觀測(cè)值殘差進(jìn)行了分析。結(jié)果表明：利用SNR能在一定程度上修正載波相位觀測(cè)值中的多路徑誤差,提高定位解算的精度。LC觀測(cè)值殘差的能量主要集中于0.0001-0.0005Hz的低頻區(qū)間,與多路徑環(huán)境誤差的特征頻率區(qū)間較為一致。3.提出了基于SNR觀測(cè)值探測(cè)冰雪引起GNSS站點(diǎn)坐標(biāo)序列異常值的算法,根據(jù)SNR值識(shí)別位置時(shí)間序列中的相應(yīng)粗差并進(jìn)行算法改正。利用美國(guó)大陸板塊邊界觀測(cè)網(wǎng)(Plate boundary observation, PBO) GPS站點(diǎn)的觀測(cè)數(shù)據(jù)對(duì)該算法進(jìn)行了驗(yàn)證和分析。異常探測(cè)算法改正結(jié)果表明：在站點(diǎn)線性構(gòu)造運(yùn)動(dòng)的假設(shè)下,坐標(biāo)序列最佳線性擬合的RMS值從改正前的0.29cm、0.16cm和1.67cm (E、 N、V)減小到0.12cm、 0.11cm和0.44cm,算法改正后的站點(diǎn)位置估算值精度得到了有效改善。4.研究了GNSS信噪比多徑干涉反演土壤濕度的函數(shù)描述模型,并對(duì)信噪比數(shù)據(jù)質(zhì)量及其選取策略、有效反演區(qū)域的確定等問(wèn)題進(jìn)行了深入探討。在此基礎(chǔ)上,設(shè)計(jì)和實(shí)現(xiàn)了基于Matlab平臺(tái)的GNSS土壤濕度反演程序包GNSS_SMI.結(jié)合實(shí)例進(jìn)行了基于GNSS多徑相位的土壤濕度反演,利用模擬仿真及現(xiàn)場(chǎng)實(shí)測(cè)土壤濕度數(shù)據(jù)對(duì)反演結(jié)果進(jìn)行了對(duì)比和分析,并對(duì)多徑相位與土壤濕度的相關(guān)關(guān)系進(jìn)行了量化描述。研究表明：土壤濕度的有效反演區(qū)域是一組與天線高、衛(wèi)星高度角和方位角相關(guān)的橢圓,選擇L2波段高級(jí)衛(wèi)星并符合多路徑反射模型的SNR數(shù)據(jù)更有利于濕度反演；SNR相位φ為土壤濕度反演和監(jiān)測(cè)土壤濕度變化趨勢(shì)提供了重要的感應(yīng)指標(biāo),指數(shù)函數(shù)能夠較好地描述延遲相位與土壤含水率之間的映射關(guān)系。5.顧及到季節(jié)、天氣、植被、坡度等因素的短時(shí)變化對(duì)SNR相位參數(shù)的影響較小,提出一種基于滑動(dòng)時(shí)間窗口的土壤濕度估算方法。分別利用全時(shí)段數(shù)據(jù)、滑動(dòng)時(shí)間窗口預(yù)測(cè)以及滑動(dòng)時(shí)間窗口插值等3種方法反演了土壤濕度并對(duì)反演結(jié)果進(jìn)行了對(duì)比和精度分析。這3種方法的相關(guān)系數(shù)平均值分別為0.717、0.832和0.952。與全時(shí)段方法相比較,基于窗口的插值和預(yù)測(cè)方法分別上升了16.2%和32.9%,誤差L1范數(shù)分別下降了39.8%和62.0%,誤差L2范數(shù)下降了17.4%和54.6%。結(jié)果表明：相比于全部時(shí)段觀測(cè)量構(gòu)建模型的土壤濕度反演方法,利用時(shí)間窗口建模反演土壤濕度能有效地模擬短時(shí)間內(nèi)不變的測(cè)站反射環(huán)境,從而改善反演精度；基于窗口的插值方法能將誤差降低至理想的效果,但無(wú)法實(shí)現(xiàn)土壤濕度的近實(shí)時(shí)預(yù)計(jì)；基于窗口的預(yù)測(cè)方法獲取的精度略低于插值結(jié)果,但其能用于近實(shí)時(shí)的應(yīng)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.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P228.4

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