一种高动态环境GPS信号的跟踪方法及实现

鉴于在高动态环境跟踪ＧＰＳ信号所遇到的问题，分析了高动态环境跟踪ＧＰＳ信号的基本方法，讨论了通过延时锁定环和载波辅助技术进行伪码跟踪的原理，重点研究通过３阶频率扩展卡尔曼滤波算法（ＦＥＫＦ）进行多普勒频率估计并进行载波跟踪的原理，模拟分析了ＦＥＫＦ算法的动态跟踪能力和频率估计误差，提出了一种适合高动态环境要求的ＧＰＳ信号跟踪方案     

提高GPS单频相位模糊度解算成功率的新方法

相位模糊度的成功解算是应用单频接收机进行高精度快速定位的关键。为了提高整周模糊度解算成功率,本文提出了一种新的算法,包括两项措施：(1)针对仅由几个历元组成的双差法方程系数阵存在着较严重病态性的结构特性,合理设计正则化矩阵,并在此基础上运用截断奇异值法求解模糊度实数解,使模糊度实数解更稳定和可靠,接近真实值;(2)在用最小二乘降相关平差(Least—squares ambiguity decorrrelation adiustment,LAMB—DA)方法进行模糊度搜索时,在Ratio检验的基础上,应用2个分析和比较的准则,对输出的多组候选整数模糊度向量进行分析和比较,甄别出正确的整数模糊度向量。算例表明,新方法不但缩短了模糊度解算所需要的观测时间,提高了模糊度实数解的可靠性和精度,而且显著地提高了整周模糊度解算的成功率。本文为单频接收机在高精度快速动态定位甚至实时定位等方面的广泛应用,提供了新的研究思路。     

一种新的整周模糊度快速求解算法

快速求解整周模糊度是利用GPS载波相位进行精确相对定位的关键技术.基于LAMBDA算法和FASF算法,提出了一种新的整周模糊度快速求解算法.该算法充分结合LAMBDA算法和FASF算法的优点,首先运用LAMBDA算法中的空间变换的思想,然后利用FASF算法的模糊度搜索技术,提高了模糊度求解效率.通过对1 km左右基线的相对定位实验表明:算法利用几个历元的数据可以正确地求解模糊度,使基线精度达到亚厘米级.     

基于Bancroft算法的GPS动态定位非线性滤波法

由于系统线性化时存在忽略项,扩展卡尔曼滤波成为一种次优的梯度下降算法.当滤波方程病态时,求解存在发散倾向,且估计量为有偏的,而非最优估计.即使动态系统噪声为高斯噪声时,残差也不是高斯噪声.在伪距定位中,由于线性化后的伪距方程是局部解,这有可能丢失正确解值.针对GPS动态导航扩展卡尔曼滤波定位问题,基于美国Bancroft的全局性非线性"闭合式求解"最小二乘算法(Bancroft算法),本文提出一种闭合式GPS非线性代数解的卡尔曼滤波法(两步算法).该方法将GPS滤波问题中的空间与时间分离,较好地解决了非线性GPS动态定位求解问题,且获得稳定可靠的动态定位解.     

Longitudinal variations of geomagnetic and ionospheric parameters in the Northern Hemisphere during magnetic storms according to multi-instrument observations

We present a joint analysis of longitude-temporal variations of ionospheric and geomagnetic parameters at middle and high latitudes in the Northern Hemisphere during the two severe magnetic storms in March and June 2015 by using data from the chains of magnetometers, ionosondes and GPS/GLONASS receivers. We identify the fixed longitudinal zones where the variability of the magnetic field is consistently high or low under quiet and disturbed geomagnetic conditions. The revealed longitudinal structure of the geomagnetic field variability in quiet geomagnetic conditions is caused by the discrepancy of the geographic and magnetic poles and by the spatial anomalies of different scales in the main magnetic field of the Earth. Variations of ionospheric parameters are shown to exhibit a pronounced longitudinal inhomogeneity with changing geomagnetic conditions. This inhomogeneity is associated with the longitudinal features of background and disturbed structure of the geomagnetic field. During the recovery phase of a storm, important role in dynamics of the mid-latitude ionosphere may belong to wave-like thermospheric disturbances of molecular gas, propagating westward for several days. Therefore, it is necessary to extend the time interval for studying the ionospheric effects of strong magnetic storms by a few days after the end of the magnetospheric source influence, while the disturbed regions in the thermosphere continues moving westward and causes the electron density decrease along the trajectories of propagation.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

GPS/INS组合导航系统半实物仿真研究

为了研究ＧＰＳ组合导航系统的性能,利用跑车实验实时采集的惯性导航系统和ＧＰＳ的数据进行了测后仿真研究。分别进行了ＧＰＳ／ＩＮＳ位置、速度组合和伪距、伪距率组合仿真,仿真中组合Ｋａｌｍａｎ滤波器采用数值稳定性较好的Ｕ－Ｄ分解算法。最后给出了纯惯导及组合后系统的位置、速度误差仿真曲线,结果分析及相关结论。     

自适应卡尔曼滤波器在陆地车辆导航中的应用

建立了车载GPS(Global Positioning System)/DR(Dead-Reckoning)组合导航系统自适应扩展卡尔曼滤波模型及其算法,从而大大提高了车辆导航系统的定位精度.首次提出依据PDOP(位置误差系数)等GPS定位系统的输出参数,自动调整观测噪声协方差阵[WTHX]R和系统噪声协方差阵Q[WT]的大小,从而自适应地调整组合导航系统模型性能的方法,使得模型具有较强的适应性.计算机仿真及实验结果表明应用该模型具有良好的效果.      

SINS/GPS组合系统姿态角误差可观测性研究

首先建立起捷联惯导系统（ＳＩＮＳ）与全球定位系统（ＧＰＳ）相结合系统的数学模型,从研究ＳＩＮＳ／ＧＰＳ组合系统的可观测性出发,通过理论分析得出了组合系统姿态角误差可观测性很差的结论,并给出使可观测阵满秩的条件。对３种飞行条件所做仿真分析验证了理论分析的正确性。     

GPS接收机的相干干扰

简述了相干干扰的原理,分析并实验验证了GPS接收机的定位位置只与其接收天线有关,提出了4种相干干扰方法,认为分布式转发干扰方法更好。