An improved real-time cycle slip correction algorithm based on Doppler-aided signals for BDS triple-frequency measurements

Doppler, which is an instantaneous GNSS observable signal, has been proven effective in determining velocity and acceleration due to its high availability and accuracy. We propose a real-time triple-frequency cycle slip correction (CSC) method based on Doppler-aided signals because Doppler shift is time-independent and immune to cycle slips. When the sampling interval is less than 1 s, cycle slips on triple-frequency can be detected and repaired using pure Doppler data with high reliability; however, this method cannot be used when the sampling interval exceeds 1 s because the integral cumulative error of Doppler increases significantly. For such cases, a modified triple-frequency CSC approach has been developed based on the raw phase and smoothed code data that was refined using the Doppler signal. To suppress the effect of the integral Doppler error, a balance factor is introduced to adjust the contributions of the raw code and Doppler observables. After the refinement of the GNSS data, three independent combinations need be selected to detect and repair cycle slips with triple-frequency observations. Four constrained criteria have been proposed to select optimal combinations that can reduce the residual ionospheric delay (RID) and measurement noise to a low level. Finally, experiments were carried out to test the performance of the new method using real triple-frequency BDS observations (GPST: 3:15:00–5:55:00, March 23, 2018). The results show that pure Doppler can detect and repair cycle slips effectively with small intervals, and modified Hatch-Melbourne-Wübbena (HMW) method based on Doppler-aided signals can achieve 99.7% success rate in cycle slip correction with large intervals (up to 30 s).     

Comparison of RO tropopause height based on different tropopause determination methods

The tropopause is an important boundary in the Earth’s atmosphere, and has been the subject of close attention from atmosphere and climate researchers. To monitor the global tropopause using radio occultation (RO) data, there are two primary methods, one is the widely used temperature lapse rate method, and the other is the bending angle covariance transform method which is unique to RO data. We use FengYun3-C (FY3C) and Meteorological Operational Satellite Program (MetOp) RO data and European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data to determine differences in RO tropopause height calculated by these two methods. We compute biases of the RO lapse rate tropopause height (LRTH) and the RO bending angle tropopause height (BATH) relative to the ECMWF LRTH. The dependences of the tropopause height biases on tropopause height (TPH) retrieval method, latitude, season and RO mission are investigated. The results indicate that BATH show a consistent 0.8–1.2 km positive bias over the tropics and high latitude regions compared with LRTH, however, over 25° to 40° latitude in both hemisphere, BATH results are less stable. Furthermore, the mean bias between BATH and LRTH displayed a different symmetrical characteristic from 2017.12 to 2018.2 (DJF) compared to 2018.6–2018.8 (JJA). However, except for some bias over Antarctica, the mean value of both LRTH and BATH show a similar tropopause variation, indicating the consistency of both methods.     

The attitude inversion method of geostationary satellites based on unscented particle filter

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.     

Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.     

基于位姿测量不确定度的飞机对接质量评估

针对基于位姿的数字化测量辅助飞机大部件对接技术的发展与应用,对位姿测量不确定度以及基于不确定度的质量评价方法进行了研究.给出了数字化对接环境下大部件位姿的数学表达形式及意义.提出了基于协方差矩阵的位姿测量不确定度解析算法,并通过仿真算例与蒙特卡洛仿真法进行了对比,验证了该算法的有效性.给出了飞机大部件对接过程数字化测量工艺能力指数的概念,用于对测量结果的可信性进行评价;通过构建位姿测量不确定度与对接质量评估指标间的映射关系,提出了一种基于位姿测量不确定度的大部件对接质量评估方法,并以机翼-机身对接过程为案例,对方法的可行性、算法的有效性进行了验证.     

一种基于自动相关信息四维运算的飞行间隔算法设计与实现

本文介绍了无人机（UAV：UnmannedAerialVehicle）航路规划的分层模式，提出了实时航路规划中四维接近算法，对UAV与静态和动态目标的接近距离进行了分析计算，为航路规划及时提供飞行过程中的危险接近信息，并进行了数字仿真。数字仿真表明：该算法具有较高的运算速度和较好预测精度，可快速提供UAV与周围静态和动...     

对流天气下空域通行能力短缺预警及响应综述

对流天气是空域通行能力短缺的主要原因之一。为了解决这一问题,需要通过理论和实践创新,建立对流天气条件下空域通行能力短缺的预警和应急响应机制,提高空域资源配置效率。从对流天气影响信息解释转换、容量评估与预警及空域响应预案三个方面梳理了国内外研究现状,总体趋势是:对流天气逐渐通过各种解释转换模型被量化为对空域和航路的影响;对流天气下空域通行能力主要受管制员工作负荷的限制,只有准确评估对流天气下的工作负荷,才能做出正确的通行能力短缺预警;对流天气响应方案,逐渐由地面等待、增加同航迹间隔、返航、备降等高耗能的流量控制措施,转向以灵活使用空域预案确保改航方案的实施方向发展。课题的应用前景是可以化解对流天气下交通需求与空域通行能力不平衡的矛盾,提高预警准确度,减少响应前置时间,提高流量管理与容量管理协同运行的效率,减轻对流天气造成的航班延误,减少航空器运行成本,实现节能减排的要求。     

基于BLUE的多窗口谱估计

文章提出了一种基于BLUE的多窗口功率谱估计算法。该算法是在无偏估计的约束条件下，使其方差最小化而得到的特征谱的最佳线性组合。通过仿真实例分析得到，与自适应的MTM相比，该算法的计算量大大减小了。由于正弦窗具有解析的表达式，不需要进行特征值分解，并且有良好的统计性能，因此，使用正弦窗的MTM可以进一步降低计算量并改善估计性能。     

应用现代CAD技术建立形体构成教学体系的探索

本文提出传统理论和现代技术必须从理论上结合，通过对组合体理论与几何造型理论的对比分析，重新定义了基本概念，应用现代CAD技术构建了新的形体构成教学体系。