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。     

利用时间差分载波相位的GNSS/INS紧组合导航

载波相位观测值精度远高于伪距,利用时间差分载波相位的组合导航可避免整周模糊度解算问题。本文以简化的时间差分载波观测模型为基础,分析其误差特性,说明了短期内时间差分载波观测更新的特点。时间差分载波在距离域本质上是相对观测值,存在误差积累,提出了利用伪距在另一个更长的组合周期上进行系统的观测更新方法。重点推导了双周期组合情况下时间差分载波观测值的实用随机模型确定公式,并结合多普勒观测值进一步保证姿态修正的精度。将采用时间差分载波的组合方案与传统伪距组合方案进行比较。结果表明,时间差分载波精度组合系统高,在观测随机模型可靠的情况下优于传统的伪距紧组合导航,误差最大的高程方向精度提高可达47%。     

电离层延迟变化自模型化的载波相位平滑伪距算法

传统的单频载波相位平滑伪距算法因受到电离层延迟变化的影响，容易出现平滑结果发散和精度下降的问题，而现有的解决方案对精度提高有限或需要外部精密电离层改正数据的支持。本文研究了电离层的变化规律并建立回归模型，在此基础上提出了一种自模型化电离层延迟变化的单频载波相位平滑伪距算法。此算法利用伪距和载波观测量中含有的电离层延迟信息进行电离层延迟建模，从平滑伪距中扣除了历元间电离层延迟变化值，有效避免了平滑伪距的发散问题。利用自编软件GNSSer实现了电离层自模型化的载波平滑伪距算法，并采用静态与动态实测观测数据进行了定位试验和精度分析。算例结果表明：①长时段常规Hatch滤波受电离层影响非常严重；②自模型化电离层延迟可达厘米级的精度，在30 min窗口内，使用线性移动开窗拟合法效果最佳；③自模型化电离层改正可以有效消除平滑伪距电离层影响，随着时段窗口的增加，精度没有降低；④利用本文提出的算法进行逐历元单频平滑伪距单点定位，在静态与动态的NEU方向都达到了亚分米级别的定位精度，其中，动态定位测试中水平和高程方向精度为6.25和10.4 cm，比原始伪距分别提高了5.4倍和3.3倍。     

两种不同方式智能手机伪距平滑分析

针对智能手机受导航芯片和天线接收器限制、伪距观测值精度不高、粗差较多等问题,本文提出利用各观测量进行历元间求双差,以探测观测值中的粗差并予以剔除,再结合多普勒观测值和载波相位观测值平滑伪距,从数据质量和伪距平滑两方面分析影响智能手机定位精度的原因,说明智能手机载波相位观测值和多普勒观测值平滑伪距的可用性。试验结果表明：智能手机载波相位平滑伪距可有效提高定位精度;多普勒观测值平滑伪距定位精度与导航芯片获取的多普勒观测值有关,小米MI8多普勒观测值精度优于华为P30。     

多普勒平滑伪距在GPS/INS紧耦合导航中的应用

针对GPS/INS紧耦合导航中GPS伪距观测值精度较低的问题,提出了一种多普勒平滑伪距在GPS/INS紧耦合导航的应用算法。首先给出了GPS/INS紧耦合导航详细的动力学模型和观测模型,在载波相位平滑伪距的基础上推导了多普勒平滑伪距的具体算法和方差-协方差计算公式,并引入遗传算法对平滑因子进行最优估计;然后利用实测数据验证了新算法的有效性。结果表明,平滑算法能够有效提高伪距观测值的精度,从而提高组合导航的定位精度,相对于载波相位平滑伪距,多普勒平滑伪距的精度略有提高,而且多普勒平滑伪距不受周跳的影响,算法的效率更高。与此同时,伪距观测值精度的提高使其与伪距率观测值精度相当,增强了滤波系统的稳定性。     

智能手机观测值伪距平滑实时定位研究

本文针对智能手机内部天线构造、GNSS接收芯片等因素限制，手机原始观测值易受外部环境影响，观测噪声大，导致定位精度较差的问题，面向大众导航定位需求，以提高手机伪距单点定位精度为目标，在对手机载波观测值周跳探测的基础上，研究对比分析了基于载波、多普勒平滑下的伪距单点定位的性能。试验表明：载波平滑伪距和多普勒平滑伪距都可以有效提高定位精度，但多普勒平滑伪距具有更好的稳定性且定位精度更好，统计显示在东（E）、北（N）、高（U）3个方向的均方根值（RMS）分别提高了52%、62%、60%，平面精度优于1.5 m。     

GPS相位平滑伪距差分定位中若干问题的探讨

伪距差分定位方法是利用基准站的伪距差对流动站的观测伪距进行改正,得到流动站改正后的伪距观测值后再解算流动站坐标。而利用载波相位历元间的差值对伪距进行平滑,可以减少测量噪声从而提高定位精度,但这都需要在一定的条件下才能得到保证。比如粗差的探测与周跳修复（算法中要利用载波相位观测量）等问题,介绍了自行编制的软件对相关问题的处理方法,并通过试验验证了方法的有效性。     

BDS三频双差载波相位组合平滑伪距算法

针对现有的载波相位平滑伪距算法多基于GNSS双频观测值且在伪距平滑过程中易受电离层延迟累积干扰的问题,提出一种基于北斗三频观测值的载波相位平滑伪距算法。首先通过构建北斗三频优选线性组合,进一步削弱双差载波相位观测值的电离层延迟以及观测噪声;接着通过经典Hatch滤波平滑器,用优选三频双差载波相位观测值代替原始载波相位观测值对组合伪距观测值进行平滑,得到最终伪距平滑解用于伪距定位。最后采用0.004、14.4、61.7和131.6 km这4组不同基线长度的北斗三频实测数据对算法进行验证,给出了平滑前后伪距历元差对比图及算法对4组基线观测值的平滑率。结果表明,所提算法对短基线和中长基线三频观测值的平滑率均能达到90%以上。通过对4组基线条件下平滑效果进行比较分析,验证了本文中的理论推导及算法的有效性。     

GPS多普勒伪距平滑定位与测速方法

针对卫星导航系统受移动载体所处特殊工作环境及状态的影响,导致载波相位观测值发生周跳并引起载波相位平滑伪距产生较大误差的问题,该文提出一种基于高精度且不受周跳影响的多普勒频移观测值对精度相对较低的伪距观测值进行平滑的方法;采用GPS多普勒频移进行伪距平滑,并计算移动载体的位置信息;通过结合求解的位置信息与多普勒频移进一步完成移动载体速度的求取。静、动态实验结果表明:该文提出的多普勒频移平滑伪距算法能够进一步提高载体的定位和测速精度。     

一种联合载波相位观测值的GPS/BDS滤波导航算法

提出了一种基于历元间相位差分的GPS/BDS单机实时动态定位算法。该方法采用历元间载波相位差分数据准确计算出载体的位置变化量;并以此描述载体的运动状态变化,建立动态定位滤波模型的状态方程。同时以历元间载波相位差分数据与伪距数据作为主要观测值,采用扩展Kalman滤波实时估计载体的位置和钟差。采用自主编制的软件对静态与车载GPS/BDS实测数据进行处理,结果表明:采用该方法,定位结果精度优于传统的标准单点定位算法与载波相位平滑伪距算法;而且算法具有较好的稳定性,与载体的运动状态无关。     

Precision real-time navigation of LEO satellites using global positioning system measurements

Continued advancements in remote sensing technology along with a trend towards highly autonomous spacecraft provide a strong motivation for accurate real-time navigation of satellites in low Earth orbit (LEO). Global Navigation Satellite System (GNSS) sensors nowadays enable a continuous tracking and provide low-noise radiometric measurements onboard a user spacecraft. Following the deactivation of Selective Availability a representative real-time positioning accuracy of 10 m is presently achieved by spaceborne global positioning system (GPS) receivers on LEO satellites. This accuracy can notably be improved by use of dynamic orbit determination techniques. Besides a filtering of measurement noise and other short-term errors, these techniques enable the processing of ambiguous measurements such as carrier phase or code-carrier combinations. In this paper a reference algorithm for real-time onboard orbit determination is described and tested with GPS measurements from various ongoing space missions covering an altitude range of 400–800 km. A trade-off between modeling effort and achievable accuracy is performed, which takes into account the limitations of available onboard processors and the restricted upload capabilities. Furthermore, the benefits of different measurements types and the available real-time ephemeris products are assessed. Using GPS broadcast ephemerides a real-time position accuracy of about 0.5 m (3D rms) is feasible with dual-frequency carrier phase measurements. Slightly inferior results (0.6–1 m) are achieved with single-frequency code-carrier combinations or dual-frequency code. For further performance improvements the use of more accurate real-time GPS ephemeris products is mandatory. By way of example, it is shown that the TDRSS Augmentation Service for Satellites (TASS) offers the potential for 0.1–0.2 m real-time navigation accuracies onboard LEO satellites.     

变分优化的高斯混合滤波及其在导航中的应用

针对时间差分载波相位/捷联惯导紧组合系统在非高斯噪声环境工作时，采用高斯混合滤波遇到的混合模型参数估计问题，提出了一种变分贝叶斯学习优化的高斯混合自适应滤波算法。该算法借鉴变分学习理论，准确高效地实现了高斯混合模型参数的自适应估计，进一步精化了滤波算法中的随机模型，能够显著提高估计精度，降低计算负担，改善滤波性能。实验结果表明，相比传统滤波算法，该算法的估计精度得到了进一步改善，运算耗时仅与拓展卡尔曼滤波相当。     

两种估计方法的GPS静态单点定位处理与实验分析

摘要GPS精密单点定位技术已经发展成为GPS领域研究的热点之一。本文主要讨论了：情密单点定位的数学模型、误差处理理论和数据预处理等,并利用Matlab编程进行实验性验证,最终对静态单点：乏位分别采用双频载波相位平滑后的P码伪距组成消电离层组合的最小二乘估计和卡尔曼滤波估计进行处理,得到亚米级的定位结果。     

带约束条件的自适应滤波及其在GPS定位中的应用

推导了约束状态下的卡尔曼滤波递推方程,采用不消去状态参数的方法,在卡尔曼滤波的数学模型中增加约束状态方程推导出约束状态下的卡尔曼滤波递推方程.表明采用带约束条件的滤波递推过程与一般卡尔曼滤波递推方程相似,只要对预报值及其协方差增加一项约束条件改正项即可,因此,在滤波计算上不需要做大的修改.还讨论了带约束条件的卡尔曼滤波的自适应算法,说明一般自适应滤波算法同样适用于带约束条件的滤波,因此在应用上非常便利.利用一组GPS动态定位数据中的伪距观测值进行计算分析,并以距离作为一个约束条件,结果显示约束条件对滤波结果的改善程度与约束条件和动态系统本身有关.对于一般卡尔曼滤波中因模型确定误差和动态目标突然加速而导致的滤波发散现象,如果增加约束条件的约束力较小时,同样会出现滤波结果偏离,因此,带约束条件的滤波同样需要考虑滤波的自适应性.     

Algorithm for carrier-adjusted DGPS positioning and some numerical results

This paper derives a DGPS positioning algorithm, referred to as the algorithm for carrier-adjusted DGPS positioning. This algorithm can be applied by a DGPS user when code and carrier observations are available and when the dynamic behaviours of both mobile positions and receiver-clock biases can and cannot be modelled. Since the algorithm directly uses code and carrier observations, the stochastic model of observations has a simple structure and can be easily specified. When the dynamic behaviour of mobile positions can be modelled, the algorithm can provide recursive solutions of the positions, on the other hand, when the behaviour cannot be modelled, it can provide their instantaneous solutions. Furthermore, the algorithm can integrate with a real-time quality-control procedure so that the quality of the position estimates can be guaranteed with a certain probability. Since in the use of the algorithm there always exist redundant observations unless the position parameters are inestimable, the quality control can even be performed when only four satellites are tracked. Using the algorithm and real GPS data collected at a 100-km baseline, this contribution investigates how DGPS positioning accuracies vary with the type of observables used at reference and mobile stations, and how important it is to choose an elevation-dependent standard deviation for code observations in DGPS data reduction. It was found that using carrier observations along with code observations is more important at the reference station than at the mobile station. Choosing an elevation-dependent standard deviation for code observations can result in better positioning accuracy than choosing a constant standard deviation for code observations. For the 100-km baseline, half-metre single-epoch positioning accuracy was achieved when dual-frequency data was used at both reference and mobile stations. The positioning accuracy became better than 0.75m when the types of observable used at the mobile station were replaced by L1 code and carrier. Received: 9 April 1996 / Accepted: 6 February 1997     

Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63–84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10–30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters.     

A comparison of P code and high performance C/A code GPS receivers for on the fly ambiguity resolution

Carrier phase ambiguity resolution on the fly is investigated using two receiver technologies, namely dual-frequency P code and high performance, single frequency, C/A code receivers. Both receiver types were used simultaneously in a series of land kinematic trials. A least-squares search technique is used to find the correct double difference carrier phase ambiguities. Both C/A and single frequency P code technologies are found to be equivalent and capable of resolving the integer ambiguities on the fly using some 30 to 200 seconds of data under benign multipath conditions. Successful ambiguity resolution on the fly results in cm-level accuracy kinematic positioning. The ambiguity resolution time required and success rate are however found to be strongly dependent on the level of carrier phase multipath and, as a consequence, on the error variance assigned to the carrier phase measurements. The use of widelaning with the dual frequency P code results in ambiguity resolution in seconds. The performance of widelaning is also superior in a comparatively high carrier phase multipath environment.     

卫星导航定位卡尔曼滤波时延平滑方法研究

为进一步提高卫星导航定位精度和性能,介绍和讨论了卡尔曼滤波延时平滑方法,并建立了一种卡尔曼滤波延时平滑定位模型与算法。与经典卡尔曼滤波类似,该算法同样具有状态系统与测量系统两类方程,能够根据后续历元测量值及定位结果向前进行逆向预测与更新,从而达到对整体定位过程再次平滑的目的,更大程度地获得定位结果跟随性与平滑性之间的平衡。通过多种不同情况下的GPS实测试验,对方法的正确性和可行性进行了分析与验证。结果表明,卡尔曼滤波延时平滑定位方法能够适用于多种静态、动态导航应用领域,使输出结果更为平滑和准确,在有效提高定位精度的同时具有更强的抗多径和抗干扰性。     

Integrated GPS/INS navigation system with dual-rate Kalman Filter

A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30?s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment.