GPS信号跟踪的多径影响分析与仿真

多径信号是GPS定位的主要误差源之一。码和载波跟踪环是GPS接收机的重要组成部分，它们直接决定了接收机的性能，因此当前多径消除技术研究的核心即是从跟踪环内部着手研究。本文仔细分析了GPS接收机信号跟踪环以及多径信号对跟踪精度的影响，对伪距码与载波相位多径误差进行了比较，并且通过仿真结果和图表阐明了码和载波相位多径误差之间的协同关系。     

一种改进扩展卡尔曼滤波算法的GPS多径抑制应用

在用EKF算法消除微弱GPS卫星信号中的噪声,并估计出多径幅度与码相位时延的码跟踪环中,由于各种误差的存在,从而降低了环路的跟踪性能。针对这一问题,该文在EKF算法的基础上提出了一种LSEKF算法,该LSEKF算法用最小二乘估计的原理对EKF算法所用的采样观测点进行修正,构造出一组无偏、均方误差和最小与空间分布确定的新型采样观测点,用来进行后续的滤波估计。仿真结果表明,相比EKF算法,LSEKF算法拥有更高的伪码跟踪精度,而且收敛速度更快,跟踪门限也更低。     

基于GPS单频软件接收机的捕获与跟踪算法实现

在分析基于软件无线电全球定位系统（GPS）接收机结构的基础上,研究了GPS单频软件接收机的捕获和跟踪算法,并基于MATLAB平台在PC上仿真了GPS单频软件接收机样机。信号捕获采用基于快速傅里叶变换（FFT）的并行码相位搜捕算法;信号跟踪联合使用超前滞后非相干延迟锁定环和科斯塔斯环的跟踪环结构。采用实测数据对信号捕获、跟踪算法进行了分析和验证。结果表明：仿真的GPS单频软件接收机具备基本的基带信号处理功能。     

旋转弹丸GPS信号跟踪方法研究

利用最大似然参数估计进行GPS信号的跟踪，处理相位和频率跟踪信号中合有的虚假调制成分。采用MonteCarlo方法仿真，研究滤波性能。仿真结果表明最大似然估计自适应滤波性能优于二次锁相环路。     

利用恒星日滤波改进精密单点电离层延迟提取

利用非组合精密单点定位(PPP)可以提取高精度的电离层延迟。测站多径误差会影响伪距和相位测量精度,影响实时PPP电离层延迟提取的精度以及收敛速度。对于静态观测站,利用对GPS卫星地面跟踪的时间重复性进行恒星日滤波可以消除多径误差的影响。通过事后处理提取前几日的码和载波相位残差序列,利用恒星日滤波建立多径误差改正模型,修正实时观测数据,可以改善实时电离层延迟估计性能。对IGS观测站的实测数据分析表明,应用恒星日滤波多径误差修正后,实时电离层延迟提取的精度由0.185m提高到0.028m,新进卫星的电离层参数估计收敛时间由80min减少为35min。     

一种GPS/GLONASS/INS数据融合算法

针对卫星导航系统和惯性导航系统(INS)的不同特性,提出了一种GPS/GLONASS/INS数据融合算法。采用差分自适应检测算法、改进码平均相位算法以及位置联合解算方法实现了GPS/GLONASS数据融合,借助于改进的粒子滤波器、INS误差模型建立系统状态方程和观测方程,完成GPS/GLONASS系统速度值和INS系统速度值数据融合,提高组合导航系统精度和可靠性。使用真实数据对数据融合算法性能进行仿真分析,结果表明所设计算法是有效的,能够处理非线性非高斯条件下的滤波估计,提高滤波精度和系统可靠性。     

基于GNSS多径信号的反射面参数估计算法

全球卫星导航系统(GNSS)多径信号广泛存在于城市峡谷等复杂导航定位场景中.多径信号在干扰GNSS接收机并造成系统定位精度下降的同时,也为接收机提供了周边反射面环境信息.在码相位延迟幅度联合跟踪算法(CADLL)实现GNSS多径信号感知和特征参数提取的基础上,设计实现了基于粒子滤波的反射面参数估计算法.该算法可以在GN...     

基于QBOC码的BOC信号码跟踪环路的性能分析

在Galileo系统建设和GPS现代化的进程中,为实现与BPSK（Bi-phase Shift Keying）的频谱分离,采用了BOC（Binary Offset Carrier）调制。BOC调制在码跟踪精度、多径抑制等方面比BPSK调制具有更好的性能,但其自相关函数的多相关峰特性使得在测距中可能产生偏差,因此,消除相关峰的模糊度是BOC信号接收中非常关键的问题。介绍了一种基于QBOC码的BOC信号码跟踪环路（“BOC十QBOC-BOC”）,并推导出该环路码跟踪性能的解析表达式,最后的仿真结果验证了理论推导的正确性,可为BOC信号接收机的设计提供参考。     

GNSS信号多径误差非包络评估方法

针对全球导航卫星系统(global navigation satellite system,GNSS)信号体制评估过程中,多径误差包络评估方法给出了多径误差的上界,并没有反映反射信号载波和子载波相位引入的码跟踪误差问题,提出了一种码跟踪多径误差非包络评估方法。该方法将反射信号的码相位延时映射到载波和子载波相位延时,为码跟踪多径误差提供了准确的理论值,而且避免了多径误差包络方法求包络曲线的过程。给出了该方法的理论表达式,对BPSK、AltBOC、MBOC和BOC信号进行了多径性能仿真,并与多径误差包络评估方法进行了对比。理论和仿真结果表明,本文方法能准确的估计码跟踪多径误差,为GNSS信号多径误差测量值提供有效的理论指导。     

双估计跟踪偏差的理论分析

BOC信号已被广泛选用于下一代卫星导航系统中,各种无模糊的码跟踪算法相继被提出。其中双估计算法由于其较好的码跟踪性能和健壮性很有可能成为BOC信号接收算法的标准。但该算法基于二维相关函数,难以对实际信道下的跟踪偏差进行理论分析。基于BOC信号的双边带信号模型首次分析了双估计算法的码跟踪偏差。理论分析证明,电离层的色散效应和通道非理想特性会导致较大的测距偏差。仿真结果验证了分析的正确性。该结论对BOC信号跟踪算法的研究具有参考价值。     

GNSS导航信号的开环补偿多径抑制方法

多径的存在会给全球导航卫星系统的接收机带来较大的定位误差。因此,高精度的接收机须对多径信号进行抑制。针对目前常用的多径抑制方法的优缺点,提出了一种基于多门延迟和曲线拟合的多径抑制方法。该方法通过多门延迟来重塑伪码的自相关函数,用于找到直射信号的伪码真实位置和接收机码跟踪环路鉴别结果之间的偏差,进一步通过曲线拟合方法更加精确地计算出该偏差,最后将该偏差通过开环方法补偿给伪距计算,使得接收机在不改变环路跟踪性能和抗动态干扰性能的前提下实现定位性能的提升。仿真结果表明新算法在前端带宽的影响下对短、中长多径均能进行有效地抑制。      

削弱GNSS多路径效应的半天球格网点建模方法

多路径效应与测站观测环境相关,无法利用差分的方式消除或削弱,是GPS精密数据处理时的主要误差源。为此,本文提出了一种利用半天球格网点模型削弱测站多路径误差影响的新方法MHGM(multi-point hemispherical grid model)。该方法对以测站为球心的半天球进行格网点划分,通过格网点的参数化描述,实现测站处多路径误差建模,可适用于现有各类GPS数据处理的软硬件设备和观测环境。本文算例的测试结果表明,采用新方法建模后双差观测值残差的RMS均值改善率平均在73.9%左右,较传统恒星日滤波方法的改善效果平均可提升26.9%,对于静态观测数据,按实时动态相对定位处理模式可获得平面精度约1.7 mm,高程精度约3.0 mm的定位结果。此外,MHGM模型还可进一步用于对测站周边可能存在的多路径误差源进行方位评估,对从物理意义上消除多路径误差源的影响给出一定的指导性意见。     

基于交叉证认的EMD滤波及其在GPS多路径效应中的应用

利用交叉证认的方法,提出一种新的基于EMD滤波去噪方法,并将其应用于GPS多路径效应的研究中。通过模拟实验及实测数据分析表明,该方法能够自适应地选择IMF中的信号层数,削弱随机噪声,合理地分离信号和噪声。利用该滤波方法去噪并建立具有重复性的多路径误差效应改正模型,可有效地削弱多路径效应的影响,进而提高GPS动态变形监测的精度。     

A new method to mitigate multipath error in single-frequency GPS receiver with wavelet transform

One of the major errors in high-precision GPS positioning is multipath. Multipath effect modeling and reduction have been a challenging issue in high-accuracy GPS positioning applications due to its special properties. Different methods have been employed to mitigate this error including hardware and software approaches. We reduce C/A code multipath error by adopting an efficient software method which uses wavelet transform as a basic data processing trend. The key idea of the proposed method is using stationary wavelet transform (SWT) in GPS signal data processing. Since we have used SWT, there is complete access to high-frequency and low-frequency terms in both time and frequency domains, and we can apply appropriated procedures to mitigate this error. The multipath error mostly is a low-frequency term. In our proposed method, the double difference (DD) residuals are applied to the SWT to identify the multipath disturbance. The extracted multipath is then used to correct DD observations. Our experiments include three data sets to investigate the proposed method and compare it with existing algorithms. We used simulations for two of these data sets. The results indicate the efficiency of the proposed method over existing algorithms.     

Filtering GPS time-series using a Vondrak filter and cross-validation

Multipath disturbance is one of the most important error sources in high-accuracy global positioning system (GPS) positioning and navigation. A new data filtering method, based on the Vondrak filter and the technique of cross-validation, is developed for separating signals from noise in data series, and applied to mitigate GPS multipath effects in applications such as deformation monitoring. Both simulated data series and real GPS observations are used to test the proposed method. It is shown that the method can be used to successfully separate signals from noise at different noise levels, and for varying signal frequencies as long as the noise level is lower than the magnitude of the signals. A multipath model can be derived, based on the current-day GPS observations, with the proposed method and used to remove multipath errors in subsequent days of GPS observations when taking advantage of the sidereal day-to-day repeating characteristics of GPS multipath signals. Tests have shown that the reduction in the root mean square (RMS) values of the GPS errors ranges from 20% to 40% when the method is applied.     

GPS multipath mitigation: a nonlinear regression approach

Under the assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formulating the multipath estimation as a regression problem, we construct a nonlinear continuous model for estimating multipath error based on well-known sparse kernel regression, for example, support vector regression. We will empirically show that the proposed method achieves state-of-the-art performance on code multipath mitigation with 79 % reduction on average in terms of standard deviation of multipath error. Furthermore, by simulation, we will also show that the method is robust to other coexisting signals of phenomena, such as seismic signals.     

Quantitative evaluation of multipath rejection capabilities of GNSS antennas

Multipath error remains the largest error source in many high precision GPS applications. To counteract this problem, solutions at both software and hardware level have been studied. Software processing by means of measurement redundancy or error predictability can be used in order to mitigate the multipath effects. In general, these techniques work properly only when the length of a reflection path exceeds that of the direct path by more than 10–20 m. Unfortunately, in most cases, reflections are generated in the area near the receiving antenna. For this reason, multipath rejection actuated at the antenna level is one of the most valid means to improve the accuracy of GPS systems. The scope of this work is twofold. First, a review of low-multipath reception requirements will be proposed for comparing different classes of high precision GNSS antennas. Based on this discussion, we introduce a quantitative evaluation of multipath rejection capabilities of a GNSS antenna. The proposed assessment technique is focused on the antenna pattern, but, in contrast to other parameters evaluating the antenna radiation characteristics, it is specifically conceived to capture the effects of multipath signals.     

GPS多路径误差处理技术

GPS高精度定位和授时中存在多路径效应。本文介绍了多路径效应产生的原理、作用特性,并着重从三个方面归纳、分析比较了目前国内多种关于多路径误差的处理技术。本文在北京基站进行了GPS单站定时实验,分析实验结果认为,多路径效应是GPS授时中不可忽视的误差因素。     

Wavelet packets based denoising method for measurement domain repeat-time multipath filtering in GPS static high-precision positioning

Repeatable satellite orbits can be used for multipath mitigation in GPS-based deformation monitoring and other high-precision GPS applications that involve continuous observation with static antennas. Multipath signals at a static station repeat when the GPS constellation repeats given the same site environment. Repeat-time multipath filtering techniques need noise reduction methods to remove the white noise in carrier phase measurement residuals in order to retrieve the carrier phase multipath corrections for the next day. We propose a generic and robust three-level wavelet packets based denoising method for repeat-time-based carrier phase multipath filtering in relative positioning; the method does not need tuning to work with different data sets. The proposed denoising method is tested rigorously and compared with two other denoising methods. Three rooftop data sets collected at the University of Nottingham Ningbo China and two data sets collected at three Southern California Integrated GPS Network high-rate stations are used in the performance assessment. Test results of the wavelet packets denoising method are compared with the results of the resistor–capacitor (RC) low-pass filter and the single-level discrete wavelet transform (DWT) denoising method. Multipath mitigation efficiency in carrier phase measurement domain is shown by spectrum analysis of two selected satellites in two data sets. The positioning performance of the repeat-time-based multipath filtering techniques is assessed. The results show that the performance of the three noise reduction techniques is about 1–46 % improvement on positioning accuracy when compared with no multipath filtering. The statistical results show that the wavelet packets based denoising method is always better than the RC filter by 2–4 %, and better than the DWT method by 6–15 %. These results suggest that the proposed wavelet packets based denoising method is better than both the DWT method and the relatively simple RC low-pass filter for noise reduction in multipath filtering. However, the wavelet packets based denoising method is not significantly better than the RC filter.