BDS-3多种授时方法精度试验及比较分析

北斗卫星导航系统(BDS)本质上是一个高精度时间空间信息服务系统,是我国 自主运行的重要空间基础设施.BDS-3已于2020年7月正式开通,向广大用户提供RNSS、SBAS、RDSS单向和RDSS双向等多种授时服务.针对BDS-3提供的各种授时服务进行了简要介绍,详细讨论了各种授时方法,并利用实测数据进行了试验验证和比较分析.结果表明,BDS-3授时服务精度全部优于公布的指标要求,其中精度最高的SBAS授时方法精度可达2ns左右,RDSS双向授时精度和RNSS授时精度相当,达到9ns左右,RDSS单向授时精度最差,在15～30ns左右.     

The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves

The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state.     

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.     

燃气轮机快速并车过程冲击及响应特性试验研究

为研究燃机快速并车过程中的冲击及响应特性,搭建并车试验台架,采用试验方法对并车参数和轴系部位对轴系动态响应幅值的影响开展研究。经过试验发现：快速并车过程中SSS离合器(同步自动换挡离合器)啮合瞬间会产生扭矩冲击,轴系因此产生明显的扭矩响应,并且相同的并车参数下扭矩响应的幅值会在一定范围内波动；降低并入机的设定扭矩或延长加载时间均可明显降低扭矩冲击和响应幅值；扭矩冲击对轴系不同部位产生的响应也不同,越靠近SSS离合器的位置,扭矩响应越大,且经过减速齿轮后响应明显降低。结果表明：所搭建的试验台架能够反映燃机快速并车过程中的冲击及响应特性,可为燃燃联合动力装置并车策略和安全性设计提供参考。     

梦天实验舱初始轨道快速计算策略与分析

初始轨道是航天器入轨评价的关键判据，快速准确计算初始轨道可在入轨异常时为应急救生控制赢得时间。针对传统初始轨道计算方法时间与精度不能兼顾的问题，设计了初始轨道快速计算策略，根据运载火箭加速度变化率来判断舱箭分离时间，采用基于动力学约束的实时轨道滑动批处理方法累积超短弧分离后数据计算初始轨道，对利用各种数据源确定的多组初始轨道进行逻辑优选判断。通过梦天试验舱仿真数据验证表明:使用该策略计算初始轨道，可达到事后精密定轨同等精度，计算时间控制在1 min以内，时效性远超事后精密轨道确定方法。     

一种提高GNSS测速精度的自适应Kalman滤波算法

在卫星导航系统动态定位中,采用基于瞬时多普勒观测量的最小二乘法确定速度,当载体高机动时,多普勒误差迅速增大,从而导致测速精度大幅度降低。针对该问题,提出一种同时实现动态模型自适应修正和观测模型自适应更新的Kalman滤波算法。算法采用滑动窗方式来建立实时更新的动态模型参数,使当前统计模型自适应地跟踪载体的动态特性。此外,算法提出观测模型的自适应更新方法,通过设置载体状态判决门限,高、中机动时仅进行受动态应力影响小的伪距更新,低机动下添加精度较高的伪距率更新。通过Sprient GSS8000模拟器产生的动态场景验证表明,相对于最小二乘法和常规Kalman滤波算法,提出的自适应Kalman滤波算法能够全面提高载体在多种运动状态下的测速精度。