本文選題：對(duì)流層延遲改正 + BP神經(jīng)網(wǎng)絡(luò)��； 參考：《東南大學(xué)》2017年碩士論文

【摘要】：對(duì)流層延遲是影響GNSS高精度定位的主要原因之一。GNSS衛(wèi)星信號(hào)經(jīng)過(guò)大氣層時(shí),受到中性大氣折射的影響會(huì)產(chǎn)生時(shí)延和路徑彎曲的現(xiàn)象,從而造成GNSS信號(hào)的傳播延遲。目前常用的改正對(duì)流層延遲的方法是模型改正法,而模型改正法又分為實(shí)測(cè)氣象模型與無(wú)氣象參數(shù)模型兩種。實(shí)測(cè)氣象模型需要獲取測(cè)站處實(shí)測(cè)的氣象參數(shù),在實(shí)際工程應(yīng)用中會(huì)受到一定的限制。而無(wú)氣象參數(shù)模型計(jì)算較為方便,但其對(duì)流層延遲改正的精度要低于實(shí)測(cè)氣象模型的改正精度。本文提出了一種基于BP神經(jīng)網(wǎng)絡(luò)技術(shù)改進(jìn)無(wú)氣象參數(shù)模型的新方法,最終建立起適用于中國(guó)地區(qū)的高精度天頂對(duì)流層延遲改正模型。本文的主要內(nèi)容和結(jié)論如下:(1)利用IGS站提供的高精度對(duì)流層延遲數(shù)據(jù),對(duì)中國(guó)地區(qū)對(duì)流層延遲的時(shí)空變化規(guī)律進(jìn)行了詳細(xì)分析。分析發(fā)現(xiàn),中國(guó)地區(qū)對(duì)流層延遲隨緯度的增加而減少,東部沿海地區(qū)的對(duì)流層延遲要高于西部?jī)?nèi)陸地區(qū),且青藏高原等高海拔地區(qū)的對(duì)流層延遲較小。(2)在分析無(wú)氣象參數(shù)的EGNOS模型和余弦函數(shù)模型的基礎(chǔ)上,利用BP神經(jīng)網(wǎng)絡(luò)技術(shù)對(duì)EGNOS模型進(jìn)行誤差補(bǔ)償,提出了一種新的IEGNOS融合模型。在中國(guó)地區(qū)5個(gè)IGS站上,IEGNOS模型的偏差絕對(duì)值的平均值和平均中誤差都要優(yōu)于EGNOS模型和余弦函數(shù)模型。其中EGNOS模型在5個(gè)IGS站上的平均中誤差為±5.5cm,而IEGNOS模型的平均中誤差為±2.9cm,相對(duì)于傳統(tǒng)的EGNOS模型,IEGNOS模型的精度提高了 47%。(3)對(duì)天頂濕延遲反演大氣可降水量PWV的過(guò)程和誤差進(jìn)行了分析。研究了利用探空氣象資料計(jì)算加權(quán)平均溫度和水汽轉(zhuǎn)換系數(shù)的方法。以探空資料計(jì)算出的水汽轉(zhuǎn)換系數(shù)當(dāng)作真值,利用最小二乘法來(lái)建立適用于云南地區(qū)無(wú)需氣象參數(shù)的Emardson水汽轉(zhuǎn)換系數(shù)計(jì)算模型,即IEmardson模型。Emardson模型在云南地區(qū)4個(gè)站的平均中誤差為±0.00495,IEmardson模型的平均中誤差為士0.00112,相比于Emardson模型,其精度提高了約77%。因此,該IEmardson模型更適用于云南地區(qū)反演大氣可降水量PWV。(4)利用IEGNOS模型來(lái)反演昆明地區(qū)的大氣可降水量。通過(guò)IEGNOS模型得到測(cè)站天頂對(duì)流層濕延遲,結(jié)合改正后的Emardson水汽轉(zhuǎn)換系數(shù)計(jì)算模型獲取昆明地區(qū)一年的大氣可降水量PWV,并與探空資料獲取的大氣可降水量PWV進(jìn)行了對(duì)比。其12個(gè)月的月平均可降水量的平均偏差為1.38mm,平均中誤差為±3.58mm,與探空資料獲取的大氣可降水量的變化趨勢(shì)基本一致,因此在無(wú)實(shí)測(cè)氣象參數(shù)時(shí)其反演的大氣可降水量PWV具有較高的可信度。
[Abstract]:Tropospheric delay is one of the main reasons that affect GNSS high precision positioning. When GNSS satellite signals pass through the atmosphere, the phenomenon of delay and path bending will occur due to the influence of neutral atmospheric refraction, resulting in the propagation delay of GNSS signal. At present, the commonly used method to correct tropospheric delay is the model correction method, and the model correction method is divided into two kinds: the measured meteorological model and the non-meteorological parameter model. The measured meteorological model needs to obtain the measured meteorological parameters at the station, which will be limited in practical engineering application. However, the accuracy of tropospheric delay correction is lower than that of measured meteorological model. In this paper, a new method based on BP neural network to improve the model without meteorological parameters is proposed. Finally, a high-precision zenith tropospheric delay correction model suitable for China is established. The main contents and conclusions of this paper are as follows: (1) based on the high-precision tropospheric delay data provided by IGS station, the temporal and spatial variation of tropospheric delay in China is analyzed in detail. It is found that the tropospheric delay decreases with the increase of latitude in China, and the tropospheric delay in the eastern coastal area is higher than that in the western inland area. The tropospheric delay of Qinghai-Xizang Plateau is small. Based on the analysis of EGNOS model and cosine function model without meteorological parameters, a new IEGNOS fusion model is proposed by using BP neural network technology to compensate the error of EGNOS model. In five IGS stations in China, the mean and mean median errors of the absolute deviation of the IEGNOS model are better than those of the EGNOS model and the cosine function model. The mean median error of EGNOS model on 5 IGS stations is 鹵5.5 cm, while that of IEGNOS model is 鹵2.9 cm. Compared with the traditional EGNOS model, the accuracy of IEGNOS model is improved 47%. The error is analyzed. The method of calculating weighted mean temperature and water vapor conversion coefficient using sounding meteorological data is studied. Taking the water vapor conversion coefficient calculated from the radiosonde data as the true value, a calculation model of Emardson water vapor conversion coefficient suitable for Yunnan region without meteorological parameters is established by using the least square method. That is to say, the mean median error of IEmardson model. Emardson model in four stations in Yunnan area is 鹵0.00495. The average median error of IEmardson model is 鹵0.00112, which is about 77% higher than that of Emardson model. Therefore, the IEmardson model is more suitable for retrieving the atmospheric precipitable water in Yunnan area. The IEGNOS model is used to retrieve the atmospheric precipitable water in Kunming area. The wet delay of tropospheric troposphere at the zenith of the station is obtained by IEGNOS model, and the annual precipitable water PWV of Kunming area is obtained by combining with the corrected Emardson water vapor conversion coefficient calculation model, and compared with the atmospheric precipitable water PWV obtained from the sounding data. The average deviation of monthly average precipitable water is 1.38 mm and the mean median error is 鹵3.58 mm, which is basically consistent with the variation trend of atmospheric precipitable water obtained from sounding data. Therefore, the inversion of atmospheric precipitable water PWV without measured meteorological parameters has a high reliability.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P228.4

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