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基于GPS卫星导航系统的物联网导航定位分析研究【物联网开题报告外文翻译说明书论文】.zip,物联网开题报告外文翻译说明书论文,卫星定位导航

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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 物联网是信息技术领域又一个重要发展，它使传统网络从人与人的联系扩展到物与物、物与人的联系，已经成为我国七大战略性新兴产业之一。目前，GPS已被应用于军事、航海、航空、测量、交通、勘测等几乎一切与位置、速度、时间有关的人类活动中。自从GPS系统建立以来，世界上对于GPS及其应用技术的研究越来越普及，我国在该领域的一些关键技术的研究也取得了一定的成果。本设计针对国内外对GPS卫星研究现状与热点，对接收机定位进行研究，论证其可行性及精度，要求学生能够熟悉掌握本设计中所涉及的卫星定位原理、卫星几何结构对定位性能的影响，使用Matlab软件完成卫星定位的解算，并通过相关实验数据对所设计算法对定位的结果进行评估。 2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）： 原始数据：（1）GPS观测的n文件和o文件；（2）利用仿真得到GPS观测的n文件和o文件技术要求：（1）利用最小二乘实现GPS单点定位解算；（2）利用卡尔曼滤波方法实现DGPS单点定位解算工作要求：（1）查阅资料30篇以上，翻译外文资料3000字以上，撰写文献综述和开题报告；（2）完成GPS单点定位算法的研究；（3）毕业设计说明书3万字左右。 毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： 毕业设计成果以毕业设计说明书形式上交，要求完成上述工作任务及达到技术要求，毕业设计说明书层次分明、论据可靠、计算正确、图标规范、语句通顺，其内容应包括仿真模型结构图、仿真结果（相关曲线图表）。 4主要参考文献： 1 吴春祥. 基于GPS的物联网智能终端定位技术研究与应用J. 东莞理工学院学报, 2015, (03):59-62.2 刘基余. GPS卫星导航定位原理与方法M. 科学出版社, 2008.3 刘海颖. 卫星导航原理与应用M. 国防工业出版社, 2013.4 Kaplan E D, Hegarty C. Understanding GPS : principles and applicationsM. Artech House, 2006.5 陈浩, 许长辉, 高井祥,等. BDS、GPS及其组合系统伪距单点定位精度分析J. 山东科技大学学报:自然科学版, 2015, 34(02):72-78.6 唐卫明, 徐坤, 金蕾,等. 北斗/GPS组合伪距单点定位性能测试和分析J. 武汉大学学报：信息科学版, 2015, 40(04):529-533.7 田安红, 付承彪, 赵珊. 一种改进的GPS测码伪距单点定位算法J. 重庆邮电大学学报：自然科学版, 2009, 21(6):736-740.8 王解先, 季善标. GPS伪距动态定位计算模型J. 同济大学学报：自然科学版, 1999, (5):530-535.9 高猛, 徐爱功, 祝会忠. 双导航定位系统伪距单点定位数据处理方法与精度分析J. 导航定位学报, 2014, (02):83-88.刘皓, 何宏, 张世璞. 卡尔曼滤波的伪距单点定位数据后处理J. 天津理工大学学报, 2010, 26(3):16-19. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：2015.12.142015.12.251、学生查阅相关文献，并在指导教师的指导下，撰写及修改开题报告、翻译专业原文资料；2、指导教师根据具体的指导情况在毕业设计管理系统中实时填写指导记录。2015.12.262016.01.03学生提交开题报告、翻译原文及译文给毕业设计指导教师指导、审阅，定稿由指导教师给出评语；对开题未通过的学生下发整改通知书。2016.01.042016.04.051、学生在指导教师的具体指导下进行毕业设计创作；2、在此阶段，在指导教师的指导下，学生拟定论文提纲或设计说明书（下称文档）提纲；3、指导教师根据具体的指导情况在毕业设计管理系统中实时填写指导记录；4、在2016年4月5日学生要提交基本完成的毕业设计创作成果以及文档的撰写提纲，作为中期检查的依据。2016.04.062016.04.101、学生提交中期课题完成情况报告给毕业设计指导教师审阅。2、各专业组织毕业设计成果验收及中期答辩。2016.04.112016.05.081、学生在指导教师的具体指导下进行毕业设计文档撰写。2、在2016年5月08日为学生毕业设计文档定稿截止日。2016.05.092016.05.10指导教师通过毕业设计（论文）管理系统对学生的毕业设计以及文档进行评阅，包括打分和评语。2016.05.112016.05.15毕业论文（设计）小组答辩。2016.05.162016.06.05根据答辩情况修改毕业设计（论文）的相关材料，并在毕业设计（论文）管理系统中上传最终稿，并且提交纸质稿。所在专业审查意见：通过负责人： 2015 年 12 月17 日 毕 业 设 计（论文） 开 题 报 告 1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于1000字左右的文献综述： 一、研究的背景和意义：物联网通用体系架构将物联网分成感知层、 网络层、 支撑层、应用层的分层结构，在未来复杂的异构网络环境下，对“物”进行精准的定位、跟踪和操控，从而实现全面灵活可靠的人-物通信、物-物通信。物联网感知层主要实现对物理世界信息的采集，其中一项重要信息就是位置信息，该信息是很多应用甚至是物联网底层通信的基础。位置信息并不仅仅是单纯的物理空间的坐标，通常还关联到该位置的对象以及处在该位置的时间，要实现任何时间、任何地点、任何物体之间的连接这一物联网发展目标，位置信息不可或缺，如何利用定位技术更精准更全面地获取位置信息，成为物联网时代一个重要研究课题。 1996年美国正式发布国家GPS政策至今，卫星定位导航技术已经成为当前应用最广泛、最成熟的无线定位技术。然而，GPS定位虽然能够满足大部分商用需求，但针对移动定位，仍存在精度低、耗时长、环境受限等无法弥补的缺陷。随着无线通信技术的发展，基于网络信息的定位技术，开启了移动定位的新篇章。同时，针对之前的定位盲区室内环境的定位技术近几年也引发业界的强烈关注。定位技术正向着更全面、更精准的方向不断前行。工信部在物联网“ 十二五” 发展规划中提出要在智能工业、农业、物流、 交通、电网、环保、安防、医疗、家居九大重点领域开展应用示范工程，探索应用模式。定位技术作为物联网的一项重要感知技术，借助其获取物体的即时位置信息， 可以衍生一系列基于位置信息的物联网应用。特别是在交通、物流领域，物体的位置实时变化，采集的其他信息通常必须与位置信息关联才有价值，因此，定位技术在智能交通、物流领域得到广泛的应用和发展。而在医疗领域中，要实现对众多的流动医疗资源和病患的实时跟踪和管理，同样也需要依赖于定位技术。据物联网应用与产业发展监测数据显示，2012年，我国物联网产业市场规模达到3650亿元，同比增长39%，发展势头强劲，预计2017年将超过万亿元级，到2020年，中国物联网产业将经历应用创新、技术创新、服务创新三个关键的发展阶段，成长为一个超过5万亿规模的巨大产业4。定位技术作为物联网的关键技术之一，由其衍生的市场经济效益也将不容小觑。2012年我国LBS个人市场规模达到36.78亿，同比增加135%，预计2013年整体个人市场规模将达到70.3亿。随着物联网在行业应用中的不断入，作为物联网应用中核心要素之一的定位技术， 也在交通、医疗、安防等多个方面扮演着不可或缺的角色，并呈现出以下发展趋势： 1) 定位范围不断扩大，无缝覆盖的需求已开始呈现。随着定位技术在物联网行业应用范围的不断扩大，新兴应用对定位的需求已不局限于单纯的室外场景，在室内定位、多种环境下的混合定位等方面也提出了新的需求。例如门到门路径导航类应用需要实现包括车辆行驶时的室外导航、室内停车场的车位引导、用户到室内特定楼层的兴趣点导引等。 2) 定位精度趋于更高，新的应用开始出现。传统定位技术一般可以实现10100m的定位精度，经过改进的新一代定位技术则可以实现10m甚至5m以内的精确定位。定位精度的不断提升， 将催生新的应用，甚至会带来物联网产业的变革。 定位技术，无论是传统的GPS定位技术还是借助于无线网络的定位技术或者短距离无线定位技术，都有其技术优势，但也都具有一定的局限性，特别是针对物联网异构的网络和复杂的环境，未来定位技术的发展趋势必然是将多种定位技术有机结合，发挥各自的优点，不断提高定位精度和响应速度，同时扩大覆盖范围，最终实现无缝、精准、迅速、安全的定位。二、国内外研究现状我国的GPS技术起步于20世纪70年代后期，虽然起步晚于一些发达国家，但经过二十多年的发展，据有关人士估计，目前我国的GPS接收机拥有量约10万左右。而且以每年2万台的速度增加。足以说明GPS技术在我国各行业中应用的广泛性。随着GPS技术的深入发展，历经十多年，我国在应用与理论方面都得到了很大的发展。引进的GPS接收机主要应用于测绘、资源勘探等静态定位，成倍地提高了作业效率，为国家节约了大量经费，并在过去人迹罕至的高原、沙漠、海洋也获得了大量的定位成果，在国家制图、城乡建设开发、资源勘察等方面有了技术保障。近几年来我国在应用GPS技术上所取得了一定的成绩： 1.国家测绘局已完成了国家高精度GPS空间定位A、B级网，总参测绘局完成了全国一、二级GPS网，形成了我国具有厘米级精度的三维地心框架基础及我国大板块间的速度场模型；建立了我国GPS永久性跟踪站及数据处理中心，自1997年起就可发布我国GPS精密星历。 2.中国地震局、总参测绘局和国家测绘局共同完成了高精度地壳运动监测网的建设，为监测板块运动和区域地壳形变奠定了基础。 3.交通部门为船舶导航技术更新，在我国沿海建立10个差分台站，实现了近海精密GPS导航。在国外，以GPS为代表的卫星导航应用产业已成为国际公认的八大无线产业之一，随着技术的进步和应用需求的增加，GPS以其强大的功能涉足众多领域，使GPS成为继蜂窝移动通信和互联网之后的全球第三个IT经济新增长点。在民用领域，除导航跟踪之外，对警察、消防、医疗等部门的救援和引导有得天独厚的优势，对于人身受到攻击的报警，特殊病人、儿童的监护与救助生活中各种困难的求助等更是非常方便。在军事领域，GPS已从当初的军舰、飞机、战车、地面作战人员提供全天候、连续实时、高精度的定位导航，扩展到成为目前精确制导武器复合制导的一种重要技术手段，概率误差大大降低，制导精度大大提高。 参考资料： 1. Klobuchar J A. Ionospheric Time-Delay Algorithm for Single-Frequency GPS UsersJ. IEEE Transactions on Aerospace & Electronic Systems, 1987, AES-23(3):325-331. 2. 李明华, 李传中. 物联网物品流动中一种基于GPS/GSM的经济实用监控技术研究J. 中国制造业信息化：学术版, 2009, 38(11):44-47. 3. Koch K R. Maximum likelihood estimate of variance componentsJ. Bulletin Godsique, 1986, 60(4):329-338. 4. Yang Y X, Li J L, Wang A B, et al. Preliminary assessment of the navigation and positioning performance of BeiDou regional navigation satellite systemJ. Science China Earth Science, 2013, 57(1):144-152. 5. Eueler H J, Goad C C. On optimal filtering of GPS dual frequency observations without using orbit informationJ. Bulletin Godsique, 1991, 65(2):130-143. 6. Weir J M H, Johnson E A, Miyanishi K. Fire frequency and the spatial age mosaic of the mixed-wood boreal forest in Western Canada.J. Ecological Applications, 2000, 10(4):1162-1177. 7. Gerdan G P. A comparison of four methods of weighting double difference pseudorange measurementsJ. Australian Surveyor, 2012, 40(40):60-66. 8. Hartinger H, Brunner F K. Variances of GPS Phase Observations: The SIGMA- ModelJ. Gps Solutions, 1999, 2(2):35-43. 9. Hegemann E. Die Ausgleichungsrechnung nach der Methode der kleinsten QuadrateJ. Science, 2006, 26. 10. Hoque M M, Jakowski N. An alternative ionospheric correction model for global navigation satellite systemsJ. Journal of Geodesy, 2015, 89(4):391-406. 11. Kizilsu G, Sahin M. SLR precision analysis for LAGEOS I and IIJ. Earth Planets & Space, 2000, 52(10):789-794. 12. 刘红平. 基于GPS的嵌入式定位器在物联网中的应用J. 计算机测量与控制, 2013(01):260-262. 13. 吴春祥. 基于GPS的物联网智能终端定位技术研究与应用J. 东莞理工学院学报, 2015(3):59-62. 14. 刘顺清. 基于北斗/GPS的物联网可视化技术研究J. 物流技术：装备版, 2015, 34(10):215-218. 15. 张波. 基于ARM的GPS接收系统的设计研究D. 河北工业大学, 2011. 毕 业 设 计（论文） 开 题 报 告 2本课题要研究或解决的问题和拟采用的研究手段（途径）： 一、主要研究问题： 1.了解GPS定位系统与北斗定位系统的区别与联系； 2.了解定位系统在物联网中的运用； 3.了解GPS与北斗定位系统对物联网导航系统的影响； 4.设计一套基于GPS/北斗定位系统的物联网导航系统，实现定位功能以及智能导航功能。二、主要研究手段： 1.阅读相关文献； 2.在网络查找相关资料； 3、学习关于GPS方面的知识； 4. 为系统制定初步设计方案； 5.进行GPS导航程序移植； 6.对整个系统进行系统联调，解决其中出现的问题。 毕 业 设 计（论文） 开 题 报 告 指导教师意见：1对“文献综述”的评语：文献综述的阅读量和相关性符合要求，文献综述能清楚表达原文献的相关观点。文献翻译的英文文献与本专业相关，翻译量符合要求，译文能表达原文的含义。2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：选题与本专业方向相关，符合要求。选题难度中等，要完成好，则工作量较大。 3.是否同意开题： 同意 不同意 指导教师： 2016 年 01 月 08 日所在专业审查意见：同意 负责人： 2016 年 04 月 22 日说明：要求学生结合毕业设计（论文）课题参阅一篇以上的外文资料，并翻译至少一万印刷符（或译出3千汉字）以上的译文。译文原则上要求打印（如手写，一律用400字方格稿纸书写），连同学校提供的统一封面及英文原文装订，于毕业设计（论文）工作开始后2周内完成，作为成绩考核的一部分。A Precise Weighting Approach with Application to Combined L1/B1 GPS/BeiDou PositioningAbstractThe BeiDou system has been providing a regional navigation service since 27 December 2012. The Global Navigation Satellite System (GNSS) user community will benefit from combined Global Positioning System (GPS)/BeiDou positioning due to improved positioning accuracy, reliability and availability. But to achieve the best positioning solutions, precise weights of the GPS and BeiDou observations are important since this involves the processing of measure-ments from two different satellite systems with different quality. Currently, a priori variances are typically used to determine the weights of different types of observations. However, such an approach may not be precise since many un-modelled errors are not accounted for. The Helmert variance component estimation method is more appropriate in this case to determine the weights of GPS and BeiDou observations. This requires high redundant observations in order to obtain reliable solutions, which will be a concern in the case of insufficient numbers of visible satellites. To address this issue, a weighting approach is proposed by a combination of the Helmert method and a moving-window average filter. In this approach, the filter is applied to combine all epoch-by-epoch weight estimates within a time window. As a result, more precise and reliable weights for GPS and BeiDou observations can be obtained at every epoch. Both static and kinematic tests in open sky and under tree environments are conducted to assess the performance of the new weighting approach. The results indicate significantly improved positioning accuracy.Key words: 1. GPS.2. BeiDou.3. Precise weighting.4. Combined GPS/BeiDou positioning.1. INTRODUCTIONWith its rapid development, the BeiDou Navigation Satellite System has attracted increasing attention from the Global Navigation Satellite System (GNSS) community. The end of 2020 will complete the full BeiDou system deployment with a constellation of five Geostationary Earth Orbit (GEO) satellites, twenty-seven Medium Earth Orbit (MEO) satellites and three Inclined Geosynchronous Satellite Orbit (IGSO) satellites (CSNO, 2012). Since 27 December 2012, a regional navigation service has been officially operational with a constellation of five GEO, four MEO and five IGSO BeiDou navigation satellites (http:/www. ). The GNSS user community will benefit from combined GPS/BeiDou positioning due to improved positioning accuracy, reliability and availability. But to achieve the best positioning solutions, precise weighting of the GPS and BeiDou observations is important as it involves the processing of measurements from two different satellite systems whose measurement quality is different in terms of noise level and residual errors. Currently, a priori variances are mostly used to determine the weights of different types of observations. Such an approach however may not be precise since many un-modelled errors are not taken into account. For instance, the residual satellite orbit and clock errors are not included in the a priori variances.Many weighting approaches have been proposed to improve the GNSS positioning solutions such as the weight model based on the carrier-to-noise ratio (Brunner et al., 1999; Hartinger and Brunner, 1999; Wieser and Brunner, 2000) and the elevation-dependent weight model (Euler and Goad, 1991; Shen et al., 2009). But these weight models are designed only for use with the same type of observations. For weighting of different types or groups of observations, the Helmert (1907) variance component estimation (VCE) method has been widely used, e.g. Koch (1986), Kizilsu and Sahin (2000), Kusche (2003) and Wang et al. (2009). Although the Helmert VCE is a rigorous weighting approach, it requires high redundant observations in order to obtain reliable weight solutions. This may not be the case in the field since there are often times when there are insufficient numbers of visible satellites. To solve this problem, a weighting approach is proposed by combining the Helmert method and a moving-window average filter. Both static and kinematic tests in open sky and under tree environments are conducted to assess the performance of the new weighting approach. The results have indicated significantly improved positioning accuracy.2. A HELMERT WEIGHTING METHOD WITH A MOVING-WINDOW AVERAGE FILTERIn this section, the observation equations for combined GPS/BeiDou single point positioning (SPP) are firstly presented. Afterwards, the error corrections and compatibility issues between GPS and BeiDou systems are addressed. Finally, a Helmert weighting method with a moving-window average filter is proposed.2.1 Observation models for combined GPS/BeiDou SPP. The combined L1/B1 GPS/BeiDou single point positioning is based on the following observation model:(1)(2)where “g” refers to GPS and “b” to BeiDou; is the measured pseudorange on L1/B1 frequencies in metres; is the geometric range in metres; c is the speed of light in metres per second; dt is the receiver clock offset in seconds; dtsys is the GPS-BeiDou system time difference (GBSTD) in seconds; dT is the satellite clock offset in seconds; dorb is the satellite orbit error in metres; dtrop is the tropospheric delay in metres; dion is the ionospheric delay on L1/B1 frequencies in metres; dTGD is the time group delay (TGD) bias on L1/B1 frequencies in metres; includes the multipath error and measurement noise in metres.The ionospheric and tropospheric errors in Equations (1) and (2) are corrected using the Klobuchar model (Klobuchar, 1987) and the Saastamoinen model (Saastamoinen, 1973), respectively. The satellite position and clock offset are com-puted using the broadcast ephemeris data. The BeiDou system adopts the Chinese Geodetic Coordinate System 2000 (CGCS2000), which differs from the WGS-84 GPS coordinate system at several centimetres (Shi et al., 2012). Their difference is therefore considered negligible for pseudorange-based SPP. Regarding the time reference, the BeiDou system has adopted an independent BeiDou Timing System (BDT), which differs from GPS Time. In addition to a constant offset of 14 s (Shi et al., 2012), there exists a fractional second difference that varies in time (CSNO, 2012; IS-GPS-200F, 2011). As a result, an additional unknown parameter is needed to estimate the system time difference between BeiDou and GPS along with three other coordinate components and one receiver clock offset parameter. The TGD corrections are made using the navigation message data (CSNO, 2012; IS-GPS-200F, 2011). Based on the observation equations in Equations (1) and (2), the position coordinates can be independently estimated epoch-by-epoch using the Least Squares method.2.2 Variance component and weight matrix estimation. In this section, a weighting approach is presented which combines the Helmert method and a moving-window average filter. The Helmert VCE method is first described to estimate the variance components of each observation group using the measurement residuals obtained from the Least Squares for combined GPS/BeiDou SPP. A moving-window average filter is then introduced to improve the precision and reliability of the weight estimates.Let and denote the variance component estimates of the unit weight for GPS and BeiDou observations, respectively. They can be determined by the following equations using the Helmert VCE method (Kizilsu and Sahin, 2000; Wang et al.,2009):(3) (4)W = V1T P1V1 V2T P2V2T(5)(6)In Equation (5), V is the observation residual vector from the Least Squares, and P is the scaled weight matrix of the observations. The matrix P can be expressed as a product of a scale factor and an unscaled weight matrix P, as shown below:(7) is a weight matrix dependent on the satellite elevation angles and the following has been widely applied (Gerdan, 1995):(8)where E is the satellite elevation angles; n is the number of satellites. The elements in the matrix S can be obtained by the following equations:a11 = n1 2tr(N1N1) + tr(N1N1)2 (9)a12 = a21 = tr(N1N1N1N2)(10)a22 = n2 2tr(N1N2) + tr(N1N2)2(11)where n1, n2 are the number of the GPS and BeiDou measurements, respectively; N is the coefficient matrix of the normal equation for all GPS and BeiDou observations; N1, N2 are the coefficient matrices of the normal equation for GPS and BeiDou observations, respectively.The computation procedure includes the following steps (Kizilsu and Sahin, 2000):(1) Assign an initial equivalent scale factor (1 = 2) to GPS and BeiDou groups of measurements.(2) Compute (i = 1, 2)(3)Compute VTiPiVi (i = 1, 2) after performing the Least Square estimation, where(4) Estimate the variance components for each measurement group using Equation (3) and determine their scale factor:(12)where c is a constant, and it is set to (5)Update i by and repeat the steps (3) and (4) until the following equation is satisfied:(13)After step (5) is fulfilled, the final weight matrix of GPS and BeiDou observations is given as:(14)The weight ratio between the GPS and BeiDou measurements is determined by:(15)Since the epoch-by-epoch estimates for the variance components are not always available due to an insufficient number of visible satellites and the weight solutions based on single-epoch data may also not be precise due to low redundancy, a moving-window average filter is applied to determine the weight ratio as follows:(16)where m is the size of the smoothing window in number of epochs.The average weight ratio estimate QMW(k) is a smoothed solution over m multiple epochs within a time window from epoch (k m + 1) to epoch k. A suitable window size m is 10x15, whose effectiveness will be demonstrated through the case studies later in the paper. When a larger window size is used, more weight solutions from previous epochs will be needed in order to obtain a reliable filtered weight ratio solution.To further enhance the reliability of the smoothed weight ratio solutions, only the weight ratio solution Q at an epoch with more than six visible GPS satellites and six visible BeiDou satellites will be applied to calculate QMW(k). For an epoch with fewer than six GPS and six BeiDou satellites, the Q estimate from the last epoch will be applied as the solution for this epoch to calculate the average QMW(k). Compared to the weight ratio estimation approach on a single epoch basis, the proposed approach has two advantages. First, it improves the estimation precision of the weight ratio solution. Second, it ensures a precise weight ratio estimate is available at every epoch even with an insufficient number of visible satellites at some epochs.3. FIELD TESTS AND PERFORMANCE ANALYSIS3.1 Data acquisition. In order to test the proposed weighting approach for combined GPS/BeiDou SPP, both static and kinematic experiments were conducted in open sky and under trees, respectively. Figure 1 displays the static tests where the open sky test (left) was conducted on the roof of a tall building near the Central South University (CSU), China and the test under trees (right) was conducted in an area with trees before the Mining Building of the CSU. The kinematic tests were carried out near the Meixi Lake in Changsha as shown in Figure 2. For both static and kinematic tests, few GPS/BeiDou signals were blocked around the receiver in open sky conditions while signals were partially blocked when the observations were made under trees. In the kinematic test, the same mode receiver as the rover receiver on the vehicle was set up as a base station to help determine the required reference coordinates of the moving vehicle at cm-level accuracy using the double difference RTK (real-time kinematic) approach. The baseline length between the base station and the rover station on the moving vehicle was less than 150 m.Figure 1. Static test in open sky (Left) and under trees (Right)Figure 2. Kinematic test in open sky (Left) and under trees (Right).Two “SOUTH S82-C” receivers, manufactured by South Surveying and Mapping Instrument Inc., China, were used for the field tests. The receiver is capable of outputting observation data at 1 Hz and supports BeiDou B1/B2 and GPS L1/L2 signal reception. The static datasets were collected in open sky on 8 December 2012 and under trees on 24 March 2013, respectively. The kinematic experiments were carried out on 24 December 2012 for both open sky and tree observing conditions. All static observations were obtained at a sampling interval of 30 s while all kinematic observations were collected at 1 Hz. The elevation mask angle was set to 10. The L1/B1 pseudorange observations are used for combined GPS/BeiDou SPP.3.2 Results and analysis. Three different weighting scenarios are assessed for combined GPS/BeiDou SPP. The first one is the “a priori variance” weighting approach in which the a priori variances of the GPS and BeiDou observations are provided to determine the initial weight of the observations. The second scenario is to determine the weight ratio between GPS and BeiDou observations using the Helmert weighting approach on a single epoch basis. The third scenario is to determine the weight ratio between GPS and BeiDou observations using the proposed Helmert weighting method with a moving-window average filter.GPS precise point positioning (PPP) (Zumberge et al., 1997; Kouba and Hroux, 2001) solutions are used as coordinate references to assess the accuracy of the pseudorange-based SPP in the east, north and up directions. The P3 software package (Gao, 2004) developed at the University of Calgary is used for the PPP processing in which the final satellite orbit and clock products from the International GNSS Service (IGS) are adopted. In the kinematic test, the reference coordinates of the rover station are determined using RTK baseline processing technique after the position of the base station is obtained by the PPP technique.Figure 3 shows the root mean square (RMS) of the three-dimensional positioning errors using a 24-hour static dataset in open sky on 8 December 2012 but applying four different initial weight ratios, namely 1:1, 4:1, 9:1 and 16:1, between GPS and BeiDou measurements. It is clearly seen that the positioning accuracy degrades when the weights of the GPS pseudorange observations increase from 1:1 to 16:1. An initial weight ratio of 1:1 is therefore adopted for the first scenario, i.e. identical a priori variances are assigned to the GPS and BeiDou observations.Figure 3. RMS of positioning errors using different weight ratios between GPS and BeiDou measure ments.Figure 4 shows the epoch-wise positioning errors of combined GPS/BeiDou SPP based on the three weighting scenarios for the static open-sky test on 8 December 2012. Figure 5 indicates the corresponding number of satellites and PDOP. The results indicate that the positioning errors using three different weighting scenarios are quite consistent. But an examination on the time window that is defined by two vertical dashed lines in Figure 4 demonstrates that the positioning errors using the proposed weighting approach are significantly smaller than using the other two weighting ap-proaches. The largest position errors in the east, north and up directions are 2.086 m, 4.085 m, 11.843 m and 3.966 m, 5.825 m, 8.887 m, respectively, when the first two weighting scenarios are used. By contrast, the largest position errors are 0.854 m,1.617 m and 4.370 m when the proposed approach is applied, which are considerably smaller. After an examination on the number of visible satellites and PDOP within the time window in Figure 5, it is found that the number of GPS satellites is only six and that is why the new method can improve the positioning accuracy. In this case study, the smoothing window size is set to 10. In order to test the effect of applying different window sizes on positioning accuracy, Table 1 provides the results using the different window sizes of 1, 5, 10, 15 and 20. It is seen that the position accuracy can be improved by only several centimetres when the window size is increased from 5 to 10. When the window size is further increased to 15, the improvement becomes even less significant. This suggests that the window size of 10 is suitable, which will be applied for the rest of our data analysis.Figure 4. Epoch-wise positioning errors for static test in open sky on 8 December 2012Figure 5. Number of satellites and PDOP for static test on 8 December 2012Figure 6 further illustrates the weight ratios between GPS and BeiDou measure-ments for three different weighting scenarios. The weight ratio solutions range from 0.96 to 1.83 for the Helmert approach while the weight ratio estimates vary in a range of 1.38 to 1.77 using the new approach. There are some abnormal small weight ratios at some epochs indicated by the green lines, which are caused by the instability of the Helmert method. The instability is attributable to the failure of con-vergence due to insufficient redundant observations at these epochs. Compared to the Helmert weighting scenario, the new method is more stable and thus improves the positioning accuracy.Table 1. RMS of positionng errors using the proposed weighting approach with different smoothing window sizes.Window size (epoch)East (m)North (m)Up (m)10869274422265084324792118100830238720671508282379206220082723772061Figure 6. Weight ratios between GPS and BeiDou measurements for static test on 8 December 2012.F To test the effectiveness of the new weighting approach under limited satellite visibility conditions, an experiment was conducted in an area with trees on 24 March 2013, as shown on the right of Figure 1. Figure 7 shows the epoch-wise positioning errors of the combined GPS/BeiDou SPP. The PDOP and number of satellites are provided in Figure 8. The average number of visible GPS and BeiDou satellites are 7.2 and 9.0, respectively, leading to an average PDOP of 1.3 in the combined GPS/ BeiDou SPP. When the time window from GPS time of 5:55:30 to 6:53:00 defined by two dashed lines in Figure 7 and Figure 8 was examined, it was found that the new weighting approach improves significantly the positioning accuracy in the case of fewer visible GPS satellites.Figure 7. Epoch-wise positioning errors for static test under trees on 24 March 2013Figure 8. Number of satellites and PDOP for static test on 24 March 2013Kinematic experiments were carried out on 24 December 2012 in open sky and under trees, respectively. Figure 9 shows the positioning errors in open sky. During the test period of three hours, an average number of 7.4 GPS and 100 BeiDou satellites were available, which results in an average PDOP of 1.1. The positioning results using three different weighting scenarios agree with each other well. The positioning errors with the proposed weighting scenarios are slightly smaller for most of the time than those using the other two weighting scenarios. The degraded quality for the period 4:15 4:40 is due to the decreased number of visible satellites when the vehicle approached a building. Figure 10 provides the weight ratios between GPS and BeiDou measurements for three different weighting scenarios. The weight ratio solutions are more stable using the new weighting approach than the Helmert method. The hand-held kinematic positioning results under trees are illustrated in Figure 11. It is clear that the new approach shows slightly smaller positioning errors in comparison to the other two weighting methods, especially for the vertical component.Figure 9. Kinematic positioning errors in open sky on 24 December 2012.Figure 10. Weight ratios between GPS and BeiDou measurements for the kinematic test in open sky on 24 December 2012In order to analyse whether the smoothing window size of 10 used in the new weighting method is suitable for the kinematic test under trees, Table 2 lists the positioning accuracy using different window sizes of 1, 5, 10, 15 and 20. Similar to Table 1, the improvement of the position accuracy is insignificant when the window size is further increased from 10 to 15. This suggests again that the window size of 10 is reasonable.To further test the effectiveness of the new weighting method under the tree environment, a car-borne kinematic test was conducted on the campus road towards the west gate of the Central South University, China, on 22 December 2013. The test started at the local time 15:45 (GPS time 7:45) and lasted one and a half hours. The car carried a “Trimble NetR9” receiver with a “TRM55971.00” antenna, which allows concurrent tracking of both GPS and BeiDou signals. The same sampling rate 1 s and elevation mask angle 10 are adopted as those applied in the previous kinematic test. A similar mode of receiver as the rover receiver was set up on the roof of the Mining Building of the Central South University as a base station to help determine the reference coordinates of the rover moving vehicle. The distance between the base and rover stations is less than 1 km. The car was driven back and forth on the road along the same route. The road condition and the setup of equipment are shown in Figure 12.Table 2. RMS of kinematic positioning errors using the proposed weighting approach with different smoothing window sizesWindow size (epoch)East (m)North (m)Up (m)12133293339645168227853001101628276329101516242761290220162327612900Figure 11. Hand-held kinematic positioning errors under trees on 24 December 2012.Figure 12. Car-borne kinematic test under trees on 22 December 2013.The car-borne kinematic positioning results under trees are depicted in Figure 13. The accuracy improvement in the horizontal directions by the new weighting scenario is clearly seen during the GPS time 7:45x8:30. The improvement in the vertical direction is especially significant for almost the entire session compared to the results using the Helmert weighting scenario. In this test, the average number of GPS and BeiDou visible satellites are 5.9 and 8.6, respectively, which results in an average PDOP value of 1.4.Figure 13. Car-borne kinematic positioning errors under trees on 22 December 2013.Table 3. RMS of positioning errors for GPS-only, BeiDou-only and combined GPS/BeiDou SPP with three different weighting scenariosGPS/BeiDou (m)GPSBeiDouA prioriHelmertNewImprovement(m)(m)variancemethodmethodrate (%)Open skyStaticEast099116830859086908304North2771341427182744238713Up284724332553222620677KinematicEast116928881352112310437North344726482508250124363Up3804137512160968084912Under treesStaticEast1874226818531789157412North979165416418618758376Up5745629959225490446019Kinematic(hand-held)East1849325716692133162824North334130102800293327636Up6479433836053964291027Kinematic(car-borne)East2533380123472485222710North835343734279397337037Up15708883752976163432430Table 3 summarises the RMS of positioning errors for combined GPS/BeiDou SPP with three different weighting scenarios as well as GPS-only and BeiDou-only SPP for all static and kinematic tests. Comparing to GPS-only or BeiDou-only SPP, the combined use of GPS and BeiDou measurements significantly improves the position-ing accuracy in three coordinate components in almost all cases. For all static and kinematic tests, the proposed weighting scenario significantly improves the positioning accuracy when compared to the other two scenarios. The improvement rate with respect to the Helmert method is listed for three coordinate components in the far right column of Table 3 in which the largest improvement rate reaches 30%. Compared to the open sky environment, the improvement under trees is more significant.4. CONCLUSIONSProper weighting of GPS and BeiDou observations is important in order to achieve the best combined GPS/BeiDou positioning accuracy. Although the Helmert variance component estimation method is widely used for weight determination of different types of measurements, the method requires high redundant observations for each observation group, which cannot often be satisfied for navigation and positioning applications in the field. To solve this issue, a Helmert weighting method with a window-moving average filter is proposed. This approach makes good use of the weight estimates at previous epochs to provide a more precise and reliable weight ratio. Static and kinematic experiments have been carried out in open sky and under tree environments and the results indicate that the new weighting approach significantly improves the positioning accuracy in comparison to the Helmert approach on a single epoch basis. The maximum improvement reaches 30% in the vertical component for the kinematic test under trees. The improvement in the horizontal directions is less significant, mostly at a level of less than 15%. 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Journal of Geophysical Research, 102(B3), 50055017, doi: 10.1029/96JB03860.合成L1/B1GPS/北斗定位的应用的精确赋权方法摘 要2012年12月27日以来，北斗系统已经开始提供区域导航服务。由于改善定位精度，可靠性和可用性，全球导航卫星系统（GNSS）用户群将从全球定位系统（GPS）/北斗定位的合并中获益。但要达到最佳的定位解决方案，GPS和北斗观察的精确的权重是重要的，因为这涉及到两个不同的卫星系统的不同质量的测量的处理。目前，先验方差通常用于确定不同类型的观测值的权重。然而，这样的一种方法可能不准确，因为没有对很多未建模错误做出解释。Helmert方差分量估计方法更适合在这种情况下确定GPS和北斗观测的权重。为了得到可靠的解决可见卫星数量不足的方案，需要高冗余观测。为了解决这个问题，一个组合Helmert方法和移动窗平均滤波器的加权法被提了出来。在这种方法中，过滤器是用来在一个时间窗口内合成所有一代又一代的估计重量。其结果是，在每一个时代都能够获得更精确和可靠的GPS和北斗观测的权重。为了评估新的加权方法的性能，在开阔的天空以及树下的环境中进行静态和动态测试。结果表明定位精度得到显著的改进。关键词：1.GPS；2.北斗；3.精确权重；4.GPS与北斗定位的合成1.介绍随着北斗导航卫星系统的快速发展，其已经从全球导航卫星系统（GNSS）的社区吸引了越来越多的关注。2020年年底将完成整个北斗系统部署，包括五个地球静止轨道星座（GEO）卫星，27中地球轨道（MEO）卫星和3颗倾斜地球同步卫星轨道（IGSO）卫星。自2012年12月27日，一个包含五个GEO，四个MEO，五个IGSO北斗导航卫星的星座的区域导航服务已经正式运营了。由于定位精度，可靠性和可用性的改善，全球导航卫星系统用户群将从GPS/北斗的合成中受益。但为了达到最佳的定位解决方案，对GPS和北斗的精确的加权的观察是很重要的，因为它涉及在噪声水平和剩余误差方面测量质量不同的两个不同的卫星系统的测量的处理。目前，演绎出的方差大部分被用于确定不同类型的观测值的权重。然而这种做法可能并不精确，因为许多未建模的误差没有考虑。例如，残留的卫星轨道和时钟误差没有包括在演绎出的差异中。为了改进GNSS定位的解决方案，许多加权方法被提了出来，例如基于载波 - 噪声比的重量模型和elevationdependent权模型。但这些重模型仅仅是为了观测使用相同类型而设计的。对于权重不同类型或组的观察值，Helmert（1907年）方差分量估计（VCE）的方法已被广泛使用，例如科赫（1986年），克孜勒苏和沙欣（2000年），Kusche（2003年）和wamg等人（2009年）。虽然Helmert VCE是严谨的加权方法，它需要高冗余的观测，才可获得可靠的质量解决方案。该领域中这种现象可能并非属实，因为通常情况下可见卫星的数量不足。为了解决这个问题，一个合成Helmert方法和移动窗口平均滤波器的加权方法被提了出来。为了评估新的加权方法的性能，在开阔的天空以及树下的环境中进行静态和动态测试。结果表明定位精度得到显著的改进。2.一个具有移动窗口平均滤波器的Helmert权重法在本节中，观测方程用于合成GPS/北斗单点定位（SPP）被首次提出。随后，GPS和北斗系统之间的误差校正和兼容性问题得到处理。最后，一个具有移动窗口平均滤波器的Helmert加权方法被提出。2.1观察合成GPS/北斗SPP的模型。合并L1/B1的GPS/北斗单点定位是基于以下的观察模型：(1)(2)其中“g”指GPS和“b”指北斗; 是L1 / B1测得的伪距频率，以米为单位；是几何范围，以米为单位; c是光的速度，以米/秒为单位；dt是接收机时钟偏移；以秒为单位；是GPS，北斗系统的时间差（GBSTD），以秒为单位；dt为卫星时钟偏移，以秒为单位；是卫星轨道误差,以米为单位；是对流层延迟，以米为单位；是在L1/ B1频率的电离层延迟，以米为单位；是一次群延迟在L1 / B1频率（TGD）偏差,以米为单位；包括多路径误差，噪声测量，以米为单位。方程中电离层和对流层误差（1）和（2）分别采用Klobuchar模型和Saastamoinen模型进行校正。卫星位置和时钟偏差使用广播星历数据计算。北斗系统采用中国大地坐标系2000，与WGS-84GPS坐标系统有几厘米的偏差。它们的区别可以忽略不计是因为以伪距为基础的SPP。关于时间基准，北斗系统采用了独立的北斗授时系统（BDT），这不同于GPS时间。除了14秒恒定偏移，存在随时间变化的分数第二差值。其结果是，一个附加的未知参数是需要估算连同三个其他协调组件在内的北斗和GPS之间的系统时间差和一个接收器时钟偏移参数。该TGD更正是因为使用导航信息数据产生的。基于在方程（1）和（2）中的观测方程，所述位置的坐标可以使用最小二乘方法一代地独立估算。2.2方差成分和权重矩阵估计。在本节中，合成了Helmert方法和移动窗口平均滤波器的加权方法将被提出。首先描述用来估算每个观察组使用的从最小二乘合成GPS/北斗SPP获得的测量残差的方差分量的HelmertVCE方法。一个移动窗口平均滤波器然后被引入，用来提高配重估计的精确度和可靠性。让2 01和202分别表示单位重量的GPS和北斗调查报告的方差分量估算。它们可以通过以下方程来确定采用HelmertVCE方法：(3)当 (4)W = V1T P1V1 V2T P2V2T(5)(6)在等式（5），V是最小二乘中的观测残差矢量，P是观测到的缩放权重矩阵。矩阵P可以作为一个标尺因子和未缩放权重矩阵P的产物来表示，如下所示(7) 是取决于卫星仰角的加权矩阵，以下公式被广泛应用：(8)其中E是卫星的仰角; n是卫星的数量。矩阵S中的元素可以由下面的等式得到：a11 = n1 2tr(N1N1) + tr(N1N1)2 (9)a12 = a21 = tr(N1N1N1N2)(10)a22 = n2 2tr(N1N2) + tr(N1N2)2(11)其中n1，n2分别是将GPS和北斗测量的数目; N全部GPS和北斗观测的所有正规方程的系数矩阵;N1和N2分别是用于GPS和北斗意见正规方程的系数矩阵。计算程序包括下列步骤：（1） 分配初始等价比例因子（1=2）给要测量的GPS和北斗群体。（2） 计算i(i=1,2)（3） 如果VTiPiVi (i = 1, 2)，在进行最小二乘估计后计算。（4） 估计每个测量组使用公式的方差分量（3），并确定它们的比例因子：(12)其中c是常数，并且它被设置为。（5） i 随着的更新而更新，并重复步骤（3）和（4），直到满足下面的等式：(13)在满足步骤（5）后，对GPS和北斗观测的最终权重矩阵被给定为：(14)GPS和北斗测量之间的重量比由下式确定：(15)低冗余导致基于单历元数据的可见卫星和重量的解决方案的数量不足也可能并不精确，导致了一代代估算出的方差分量并不总是可用，移动窗平均滤波器应用于确定的重量比为：(16)其中m为在历元的数目的平稳窗口的大小。平均重量比例的估算QMW(k)是在从（k-m + 1）时期到k时期的时间窗口的多个时期中的一个平滑的方案。合适的窗口尺寸m为1015，其有效性将在后面的文章通过案例研究来证明。为了获得必要的过滤重量比例，当实用一个更大的窗口尺寸时，先前的时期的更多的重量的解决方案将被需要。为了进一步增强平滑重量比解的可靠性，在有超过六可视GPS卫星和六个可见北斗卫星的时期，仅重量比解决方法Q，将被应用以计算QMW（k）。超过六个GPS和六个北斗卫星的时期，最后时期的Q估算将被作为此时期计算平均QMW（k）的方案施加。相比在一个时期基础的重量比估计方法，该方法有两个好处。第一，它改善了重量比的估计精度解。其次，它确保了在每一个甚至是某些可见卫星数量不足的时期的精确的重量比估计。3. 场地试验和性能分析3.1数据采集。为了测试所提出的结成GPS/北斗SPP的加权方法，静态和动态实验在开放的天空和树木下的分别进行。图1显示了静态试验，其中开放天空测试（左）是在中国中南大学（CSU）附近高层建筑的屋顶进行，树下的测试（右）是在CSU的矿业建筑前的树木前的一个区域进行。运动试验是在附近的长沙梅溪湖进行，如图2。无论静态和运动试验，当观测在树下进行时，部分信号被堵塞，一些GPS/北斗信号被封锁在开阔的天空接收器周围。为了帮助在RTK（实时动态）的方法的厘米级精度确定移动车辆所要求的参考坐标，在动态测试中，相同的模式接收器作为流动站接收机被设置为基站。所述基站之间的基线长度并在移动车辆的流动站是小于150m。图1.在开阔的天空（左）和树下（右）静态测试图2.在开阔的天空（左）和下树运动试验（右）两个由中国南方测绘仪器公司生产的“SOUTH S82-C”接收器，被用于现场测试。接收器能够输出在1Hz上的观测数据和支持北斗B1 / B2和GPS L