一种双轴旋转惯性导航系统的重要参数快速自标定方法

为了提高双轴旋转惯导重要参数标定的快速性和精度，提出一种快速自标定方法。通过设置不同的标定路径可以在10 min内完成陀螺和加速度计的零偏以及标度因数误差的标定。该方法利用基于姿态误差观测的卡尔曼滤波完成陀螺零偏的估计。通过六位置翻滚并以速度误差作为观测量进行卡尔曼滤波，完成加速度计的零偏及标度因数误差的标定。使天向陀螺绕方位轴旋转4周，使水平陀螺绕水平轴转动4周，通过计算旋转前后的姿态误差完成陀螺标度因数误差的估计。仿真和试验结果表明，该方法可以实现双轴旋转惯导重要参数10 min内完成自标定，且具有较高的精度。     

基于长短时记忆神经网络(LSTM)的钟差预报算法

原子钟钟差预报在原子时计算和原子钟频率驾驭中发挥着重要的作用。长短时记忆神经网络（LSTM）预报算法能够处理多参数长期依赖关系的时间序列预报，以氢钟和铯钟实测数据为样本，通过构建LSTM钟差预报模型，降低了长期原子钟内部噪声以及原子钟漂移对钟差预报的影响，并以72h，240h和720h为预报时长，分别与线性多项式模型、灰色模型和Kalman模型原子钟钟差预报模型进行预报误差对比。研究表明，在240h以上的预报时长中，LSTM建模长期依赖关系的优势得以体现，相较于其他3类模型可以获得更高的预报精度。     

异构多星编队的脉冲构型保持控制方法

近地轨道的双星编队通常设计具有自稳定性的编队构型参数初值，通过保持编队构型参数形成长期稳定的相对周期运动。针对编队中卫星数量增多产生的相对运动耦合问题，提出了基于Hill坐标和三角函数公式的多星相对运动分析方法。基于SAR载荷测量基线定义，结合多星编队构型参数的相对运动特性，提出了编队构型参数的设计方法，能够实现多星编队的最大有效基线组合。通过分析J2项摄动和大气阻力摄动的长期影响，研究了异构多星编队的相对运动衍化规律，提出了主从形式的脉冲偏置控制，能够有效保持针对异构多星编队设计的编队构型。通过面质比异构的四星编队控制仿真，验证了脉冲偏置控制形式下异构多星编队构型保持控制方法的有效性。     

Impact of receiver inter-frequency bias residual uncertainty on dual-frequency GBAS integrity

Dual-Frequency Ground-Based Augmentation Systems (GBAS) can be affected by receiver Inter-Frequency Bias (IFB) when Ionosphere-Free (Ifree) smoothing is applied. In the framework of the proposed GBAS Approach Service Type F (GAST-F), the IFB in the Ifree smoothed pseudorange can be corrected. However, IFB residual uncertainty still exists, which may threaten the integrity of the system. This paper presents an improved algorithm for the airborne protection level considering the residual uncertainty of IFBs to protect the integrity of dual-frequency GBAS. The IFB residual uncertainty multiplied by a frequency factor is included in the Ifree protection level together with the uncertainty of other error sources. To verify the proposed protection level algorithm, we calculate the IFB residual uncertainties of ground reference receivers and user receiver based on BDS B1I and B3I dual-frequency observation data and carry out a test at the Dongying Airport GBAS station. The results show that the proposed Ifree protection level with IFB residual uncertainty is 1.48 times the current protection level on average. The probability of Misleading Information (MI) during the test is reduced from 3.2 × 10^?4 to the required value. It is proven that the proposed protection level can significantly reduce the integrity risk brought by IFB residual uncertainty and protect the integrity of dual-frequency GBAS.     

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.