Modeling and initial assessment of the inter-frequency clock bias for COMPASS GEO satellites

The COMPASS system is a project established by China to develop an independent global satellite navigation system, which has five GEO (Geostationary Orbit) satellites and thirty Non-GEO satellites. An apparent inter-frequency clock bias (IFCB) for COMPASS GEO satellites is investigated using the real data. The bias also is modeled by the different models. Based on the 15 months (DOY 121, 2011–214, 2012) single-day-estimated results, the periodic variation of IFCBs of the COMPASS GEO satellite is studied using a harmonic analysis. The notable periods of 12 h and 8 h are noted. The harmonics-based models with different periods and different orders and quadratic function based model are used to describe the IFCB. The performances show that the 4-order harmonics-based model with the periods of 24, 12, 8 and 6 h is most optimal than others for describing the IFCB of COMPASS GEO satellite. Its amplitudes and phases estimated from a least square fit are used to study the features of the IFCB. The results show that the current amplitudes and phases do not present special features. Although the irregular amplitudes and phases of the model are disadvantageous for the long-term prediction of IFCB, it is obvious that the modeling IFCB can simple its service and a few of coefficients can replace the IFCB series. The performance of the model in short-term prediction IFCB is tested using the ten-day data (DOY 215-224, 2012).     

流星雷达系统相位差偏差的估计和校正

介绍了一种新的流星雷达系统的相位偏差估计和校正方法.利用流星回波的观测数据,用回波信号在各个接收通道之间的相位差,结合干涉式接收天线阵的几何关系,建立了各天线相位差测量值与偏差值之间的线性方程组,利用最小二乘法求解方程组,得到了流星雷达系统各个接收通道之间的相位差偏差估计值及校正后的流星回波到达角.与已有的流星雷达相位偏差估计和校正的方法相比,这种方法可以通过流星雷达的观测数据来计破算雷达系统各个接收天线通道之间的相位差偏差量,而不需要增加额外的硬件,实现了对观测数据的事后处理,可以方便地对已有数据进行校正.以2004年4-6月的武汉流星雷达观测数据为例,计算了流星雷达系统的偏差估计量,并用校正后的数据来计算流星回波的空间位置.结果表明,校正后流星回波数在各个方向上随高度的分布比校正前更符合统计分布.      

Particle filter-based real-time phase line bias estimation for GNSS-based attitude determination with common-clock receivers

Global navigation satellite system (GNSS)-based attitude determination has been widely adopted in a wide variety of terrestrial, sea, air, and space applications. Recently, the emergence of commercial multi-GNSS common-clock receivers has brought new opportunities for high-precision GNSS-based attitude determination with single-differenced (SD) model. However, the key requirement of using this approach is the accurate estimation of the troublesome line bias (LB) in real-time. In this contribution, we propose a particle filter-based real-time phase LB estimation approach that apply to SD model with single-system single-frequency observations from common-clock receiver. We first analyzed the relationship between the integer ambiguity ratio value and the phase LB. It is proved that the accuracy of a given phase LB value can be qualified by the related ambiguity resolution ratio value, and the normalized ratio value can therefore be used to represent the likelihood function of observations. Then, we presented the particle filter-based real-time phase LB estimation procedure, and assessed its performance using GPS L1/BDS B1I observations from two datasets collected with different types of common-clock receivers in terms of the accuracy and convergence time of phase LB estimation, the computation load, and the positioning and attitude determination accuracy with respect to the double-differenced (DD) model. Experimental results demonstrated that the phase LB could be accurately estimated with short convergence time (generally within 15 epochs). Moreover, compared with the classical DD approach, the particle filter-based SD approach delivers comparable positioning root-mean-square (RMS) errors in the North and East components but significantly smaller RMS errors in the Up component. Accordingly, the achievable yaw accuracy is comparable whereas the pitch accuracy is remarkably improved. The improvements of positioning accuracy in the Up component and pitch accuracy are approximately 35.7 % to 63.7 %, and 33.3 % to 63.1 %, respectively. Additionally, the single-epoch computation time with our particle filter-based SD approach is generally 0.08 s, which is obviously larger than the DD approach but could still meet the requirements of real-time applications below 10 Hz sampling.