智能手机RTK定位软件实现及应用试验

随着芯片技术的发展,智能手机已成为使用最普遍的一类全球卫星导航系统(GNSS)设备,其提供位置服务的能力逐步彰显. 为探究将手机作为专业GNSS设备的可行性,利用谷歌开放Android智能终端GNSS原始观测数据这一契机,设计并实现一款手机实时动态 (RTK)定位手机应用程序(APP),并基于该APP开展高精度定位应用试验. 结果表明:在静态条件下,手机RTK定位精度约达1 dm;在行人和车载动态条件下,可达平面亚米级、高程1~2 m的精度水平,RTK定位精度远高于内置芯片解,但稳定性略差于芯片解. 使用手机模拟RTK点测量,其平面精度约达1 m,基本满足地理信息采集和调查等亚米级到米级低精度专业应用的需求.      

Android智能手机实时精密单点定位软件实现及精度分析

随着智能手机的普及和卫星导航定位技术发展,导航定位已成为智能手机必不可少的功能之一。对华为(Huawei)和小米(Xiaomi)双频智能手机Huawei Mate 20/30、Xiaomi 8观测数据进行质量分析,提出了一套适用于手机精密单点定位(precise point positioning, PPP)的质量控制方案,开发了一款基于安卓(Android)平台的实时PPP应用程序。通过实验对比分析了单频与双频、实时与事后手机PPP的定位性能。结果表明,Xiaomi 8和Huawei Mate 20/30手机单频PPP平面方向的定位精度均可达到0.5～0.6 m,高程方向的定位精度为1.0～2.0 m。相比于单频PPP,双频PPP收敛后,其平面与高程方向的定位精度均有提高,且高程方向提升显著。实时与事后PPP在平面方向的定位精度相当,但在高程方向上,事后PPP较实时PPP的定位精度提升20%～40%。     

安卓智能手机的精密单点定位模型研究

针对当前智能手机的全球卫星导航系统(GNSS)原始数据质量不佳导致常规精密单点定位(PPP)模型无法有效利用以及缺少智能手机PPP模型测试分析资料等问题,该文以载波加钟差的改进精密单点定位模型为基础,扩展至相应的载波加钟速模型、UofC模型、无模糊度模型、载波二次差模型及星间差分模型,简述了各个模型的特点,并利用华为P10智能手机的GNSS原始数据,测试并分析了各个模型定位精度情况:星间差分PPP模型在短期内(10 min)定位精度最优,平面中误差在1.8 m左右,高程2.1 m左右;改进模型与载波加钟速模型定位精度相当,UofC模型精度稍差;无模糊度模型与载波二次差模型需在Kalman滤波过程中采用降权方式防止其发散,定位精度取决于前期历元的数据质量。     

北斗广域实时精密定位服务系统研究与评估分析

采用IGS、MGEX、北斗地基增强网的实时观测数据,研制北斗广域精密定位服务系统,实时生成北斗高精度轨道、钟差、电离层产品,提供厘米级北斗双频PPP、分米级单频PPP、米级单频伪距定位服务。对实时产品评估分析的结果表明:北斗卫星实时轨道与钟差产品URE统计精度约为2.0cm,实时电离层精度优于4.0TECU。采用全国分布的实时测站动态定位精度(95%置信度)评估分析表明:北斗双频PPP精度存在明显的区域特征,高纬度以及西部边缘地区的定位精度平面约0.2m,高程约0.3m;中部地区定位精度平面优于0.1m,高程优于0.2m,接近GPS实时PPP精度水平;北斗与GPS融合可以提高单北斗、单GPS的定位性能,尤其是显著加快了PPP收敛时间,收敛时间缩短到20min内。另外,除边缘地区外,北斗单频PPP实现平面0.5m,高程1.0m;北斗单频伪距单点定位实现平面2.0m,高程3.0m。     

载波/多普勒平滑伪距在Android手机中的定位实现

针对Android手机GNSS伪距定位精度较低的问题，利用手机端观测信息，通过载波相位/多普勒平滑伪距改善手机端伪距观测值的质量，从而达到提高定位精度的效果。首先给出了Android手机GNSS原始观测量的获取方法，然后推导了载波相位平滑伪距和多普勒平滑伪距算法模型，并设计合理有效的试验对算法的精度进行评定。试验结果表明：在手机端静态定位中载波相位和多普勒平滑算法均可提高原始伪距的定位精度，且多普勒平滑算法表现更优；在手机端动态定位中多普勒平滑算法可获得比原始伪距更优的定位精度，但是载波相位平滑算法较原始伪距更差；由于硬件的制约手机端周跳和信号失锁严重，占比超过50%，载波相位在手机端的可用性较低；多普勒平滑算法的最优平滑时间常数小于等于10 s，具有实时动态定位的巨大潜力。     

复杂环境下智能手机RTK+PDR融合定位

针对室外复杂环境下智能手机定位精度低、抗干扰能力不足的问题,本文利用手机GNSS观测值和手机内置IMU数据,采用RTK和PDR算法融合定位,对比分析了小米8和华为Mate20X两款手机的GNSS数据质量和融合定位算法性能,以及不同观测条件下融合算法的定位精度和稳定性。试验结果表明,在良好和复杂两种观测条件下,采用RTK算法定位精度分别为1.8 m和4.6 m;采用RTK+PDR融合算法定位精度分别为1.2 m和2.6 m,在两种环境下,RTK+PDR融合算法的精度分别提高了50%和76%,即显著提高了智能手机在室外复杂环境下的定位精度。     

大众智能手机精密定位与结果分析

随着移动互联网的发展与智能手机的普及，大众用户对高精度位置服务的需求日益增加。目前Google公司Android操作系统已开放GNSS原始观测值接口，采用智能手机实现传统适用于专业设备的PPP、RTK等高精度定位成为可能，因此采用原始观测值进行智能手机高精度定位成为研究热点。本文基于协同精密定位服务平台提供的实时轨道、实时钟差与电离层产品信息，实现了智能手机的PPP高精度定位处理。通过实测验证表明：在理想观测条件下，主流智能手机小米8与华为P10实现PPP定位精度水平优于1 m，相对标准伪距单点定位精度分别提高36%和47%。     

Android智能手机GNSS定位性能分析

随着全球卫星导航系统(GNSS)的发展和移动通信技术的进步,用户对位置服务(LBS)提出了更高的要求. 本文采用市面上常见的两部Android智能手机采集GNSS数据,对Android智能手机伪距单点定位(SPP)和单频精密单点定位(PPP)算法进行研究,分析了在不同条件下智能手机的SPP、单频PPP定位性能. 结果表明:在使用多普勒平滑伪距和信噪比随机模型的基础上,Android智能手机GPS单系统的SPP定位精度可达3 m,GPS、Galileo、GLONASS、北斗卫星导航系统(BDS)四系统定位精度可达亚米级. 在单频PPP静态定位中,在GPS单系统下,定位精度仅能达到米级,且收敛时间较长;在GPS、Galileo、GLONASS、BDS四系统下,定位精度可达亚米级,且平面方向可在40 min内收敛. 在单频PPP动态定位中,手机的定位精度仅能达到米级.      

智能手机多频多系统实时动态的定位性能分析

随着位置服务的发展,人们对定位精度的需求不断提升. 目前智能手机定位主要依赖于全球卫星导航系统(GNSS)芯片所提供的芯片解,其精度仅为米级. 2016年,谷歌宣布允许开发者获取手机GNSS原始观测数据,为研究手机GNSS高精度定位算法提供了支持. 为探索智能手机多频多系统实时动态(RTK)的定位精度和可靠性,文中基于华为P40智能手机开展了静态和动态环境下的多频多系统RTK的定位性能分析. 结果表明:在静态环境下,智能手机多频多系统的RTK定位精度要优于芯片解,在东(E)、北(N)、天(U)三个方向的定位误差均方根(RMS)分别为0.20 m、0.39 m和0.31 m,比芯片解提高了57%、71%和75%;在动态环境下的定位精度依然能够达到分米级,相比于芯片解在E、N、U三个方向上的定位精度提高了37.84%、47.22%、53.68%.      

广播星历SSR改正的实时精密单点定位及精度分析

本文分析了利用广播星历和SSR改正信息获取实时精密星历和卫星钟差的方法,并对生成的实时产品进行了精度评估:利用IGS分析中心提供的实时NTRIP数据流SSR改正信息,基于广播星历改正RTPPP模型实现了实时静态和动态精密单点定位,并分别进行了精度分析。结果表明:将广播星历SSR改正获得的实时产品与IGS最终产品相比较,卫星轨道互差RMS值为4cm~7cm、卫星钟差互差RMS值优于0.3ns;实时静态PPP在观测时段6h以上的情况下,可实现水平方向2cm、高程方向4cm的定位精度,24h单天解的平面及高程方向精度均优于2cm;实时动态PPP的定位精度可达cm级,收敛至亚dm级精度的时间与事后PPP在不固定非差模糊度情况下所需的时间相当。     

Method for real-time self-calibrating GLONASS code inter-frequency bias and improvements on single point positioning

Utilization of frequency-division multiple access (FDMA) leads to GLONASS pseudorange and carrier phase observations suffering from variable levels inter-frequency bias (IFB). The bias related with carrier phase can be absorbed by ambiguities. However, the unequal code inter-frequency bias (cIFB) will degrade the accuracy of pseudorange observations, which will affect positioning accuracy and convergence of precise point positioning (PPP) when including GLONASS satellites. Based on observations made on un-differenced (UD) ionospheric-free combinations, GLONASS cIFB parameters are estimated as a constant to achieve GLONASS cIFB real-time self-calibration on a single station. A total of 23 stations, with different manufacturing backgrounds, are used to analyze the characteristics of GLONASS cIFB and its relationship with variable receiver hardware. The results show that there is an obvious common trend in cIFBs estimated using broadcast ephemeris for all of the different manufacturers, and there are unequal GLONASS inter-satellite cIFB that match brand manufacture. In addition, a particularly good consistency is found between self-calibrated receiver-dependent GLONASS cIFB and the IFB products of the German Research Centre for Geosciences (GFZ). Via a comparative experiment, it is also found that the algorithm of cIFB real-time self-calibration not only corrects receiver-dependent cIFB, but can moreover eliminate satellite-dependent cIFB, providing more stable results and further improving global navigation satellite system (GNSS) point positioning accuracy. The root mean square (RMS) improvements of single GLONASS standard point positioning (SPP) reach up to 54.18 and 53.80% in horizontal and vertical direction, respectively. The study’s GLONASS cIFB self-estimation can realize good self-consistency between cIFB and stations, working to further promote convergence efficiency relative to GPS?+?GLONASS PPP. An average improvement percentage of 19.03% is observed, realizing a near-consistent accuracy with GPS?+?GLONASS fusion PPP.     

一种基于智能手机的实时高精度定位系统开发与车载应用测试北大核心CSCD

手机GNSS芯片可支持多模GNSS观测信号,其提供的原始观测量为高精度导航定位提供了可能,智能手机高精度导航定位成为研究热点之一。本文首先基于自研的反向RTK算法,设计并开发了一套基于智能手机的实时高精度定位系统,降低手机的计算压力;然后基于智能手机小米8,进行了大范围(覆盖深圳、武汉、北京)、多场景(城市开阔/遮挡,高速开阔/遮挡)的动态车载应用测试,用于验证系统的可靠性和可用性。测试结果表明:系统在各场景下均能稳定有效运行,在开阔环境下,小米8可实现亚米级的实时动态定位精度,精度最优可达0.21 m。     

PPP-B2b服务的实时精密单点定位精度分析

基于北斗官方发布的承载精密单点定位(PPP)服务的PPP-B2b信号与南方测绘最新研发高精度定位终端,本文采用全国6个城市连续一周的观测数据和实时定位结果,分析了基于PPP-B2b服务的PPP精度。其中重点分析了实时静态PPP与实时动态PPP定位的精度。试验结果如下:基于PPP-B2b服务的静态PPP定位精度水平方向优于7 cm,高程方向优于10 cm;基于PPP-B2b服务的动态PPP定位精度水平方向优于10 cm,高程方向优于15 cm。试验结果表明,基于该服务的实时PPP能达到静态厘米级、动态分米级的定位精度。     

Ionospheric error contribution to GNSS single-frequency navigation at the 2014 solar maximum

For single-frequency users of the global satellite navigation system (GNSS), one of the main error contributors is the ionospheric delay, which impacts the received signals. As is well-known, GPS and Galileo transmit global models to correct the ionospheric delay, while the international GNSS service (IGS) computes precise post-process global ionospheric maps (GIM) that are considered reference ionospheres. Moreover, accurate ionospheric maps have been recently introduced, which allow for the fast convergence of the real-time precise point position (PPP) globally. Therefore, testing of the ionospheric models is a key issue for code-based single-frequency users, which constitute the main user segment. Therefore, the testing proposed in this paper is straightforward and uses the PPP modeling applied to single- and dual-frequency code observations worldwide for 2014. The usage of PPP modeling allows us to quantify—for dual-frequency users—the degradation of the navigation solutions caused by noise and multipath with respect to the different ionospheric modeling solutions, and allows us, in turn, to obtain an independent assessment of the ionospheric models. Compared to the dual-frequency solutions, the GPS and Galileo ionospheric models present worse global performance, with horizontal root mean square (RMS) differences of 1.04 and 0.49 m and vertical RMS differences of 0.83 and 0.40 m, respectively. While very precise global ionospheric models can improve the dual-frequency solution globally, resulting in a horizontal RMS difference of 0.60 m and a vertical RMS difference of 0.74 m, they exhibit a strong dependence on the geographical location and ionospheric activity.     

A reference station-based GNSS computing mode to support unified precise point positioning and real-time kinematic services

Currently, the GNSS computing modes are of two classes: network-based data processing and user receiver-based processing. A GNSS reference receiver station essentially contributes raw measurement data in either the RINEX file format or as real-time data streams in the RTCM format. Very little computation is carried out by the reference station. The existing network-based processing modes, regardless of whether they are executed in real-time or post-processed modes, are centralised or sequential. This paper describes a distributed GNSS computing framework that incorporates three GNSS modes: reference station-based, user receiver-based and network-based data processing. Raw data streams from each GNSS reference receiver station are processed in a distributed manner, i.e., either at the station itself or at a hosting data server/processor, to generate station-based solutions, or reference receiver-specific parameters. These may include precise receiver clock, zenith tropospheric delay, differential code biases, ambiguity parameters, ionospheric delays, as well as line-of-sight information such as azimuth and elevation angles. Covariance information for estimated parameters may also be optionally provided. In such a mode the nearby precise point positioning (PPP) or real-time kinematic (RTK) users can directly use the corrections from all or some of the stations for real-time precise positioning via a data server. At the user receiver, PPP and RTK techniques are unified under the same observation models, and the distinction is how the user receiver software deals with corrections from the reference station solutions and the ambiguity estimation in the observation equations. Numerical tests demonstrate good convergence behaviour for differential code bias and ambiguity estimates derived individually with single reference stations. With station-based solutions from three reference stations within distances of 22–103 km the user receiver positioning results, with various schemes, show an accuracy improvement of the proposed station-augmented PPP and ambiguity-fixed PPP solutions with respect to the standard float PPP solutions without station augmentation and ambiguity resolutions. Overall, the proposed reference station-based GNSS computing mode can support PPP and RTK positioning services as a simpler alternative to the existing network-based RTK or regionally augmented PPP systems.