Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.     

Star angle/ double-differenced pulse time of arrival integrated navigation method for Jupiter exploration

Pulsar navigation is a promising autonomous navigation system for spacecraft, which is applicable to the entire solar system. However, the pulsar’s directional error and the onboard clock error are two types of systematic errors that seriously reduce navigation accuracy. To solve this problem, a star angle/double-differenced pulse time of arrival(SA/DDTOA) integrated navigation method is proposed. Since measurements obtained by observing different pulsars contain the same clock errors, the measurements can be differed to eliminate the common clock error. Then, the pulsar-differenced measurements at neighbor filtering time can be differed to suppress the effect of the pulsar’s directional error on navigation precision. Star angle is used to obtain absolute navigation information, which denotes the angles between the light of sight of Jupiter and that of its background stars. Simulation results demonstrate that the proposed method can eliminate the influence of the onboard clock error and greatly weaken the effects of the pulsar’s directional error. The navigation accuracy is better than the traditional star angle/pulse time of arrival integrated navigation method and star angle/pulse time difference of arrival integrated navigation method. In addition, the navigation accuracy of the SA/DDTOA integrated navigation method is less affected by Jupiter’s ephemeris error. This work greatly reduces the influence of common systematic errors in pulsar navigation on navigation accuracy.     

An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %～52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %～54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.     

基于iGMAS的国家标准时间精密授时系统

UTC是国际标准时间.UTC(NTSC)是UTC的物理实现之一,是我国的国家标准时间,也是我国一切授时业务的基础.目前,广泛应用的GNSS授时精度可达10～50ns.随着现代信息社会的快速发展,数十纳秒的授时精度及事后处理的工作模式已无法满足需求.针对上述问题,设计了基于iGMAS的国家标准时间精密授时系统(PTS).PTS的基本原理为:服务端基于iGMAS平台生成实时轨道及以UTC(NTSC)为参考的实时卫星钟差,用户端结合实时产品及伪距、载波相位观测数据,解算本地钟与UTC(NTSC)偏差.此外,搭建了PTS原型系统并展开测试分析,测试结果显示,基于PTS原型系统,各用户站授时精度均优于1ns.与GNSS授时技术相比,PTS将授时精度提高了1～2个量级,且基于国家标准时间授时,成本低廉,易于实现,具有应用前景.     

无人机集群组网高精度测距技术研究

针对无人机组网系统机间测距精度受限于时域采样周期时延估计的问题，本文提出一种联合正交频分复用（OFDM）前导序列和导频序列进行高精度测距估计的方法，利用最大似然估计和双向测距法将参与计算的序列测距估计结果按比例进行加权平均。同时，仿真分析了联合测距估计方法性能。结果表明：联合估计方法的前导与导频序列差分自相关间隔长度取典型值时测距精度能达到0.1 m以下，可在较低信噪比下实现厘米级测距精度，提升了集群组网测距的稳定性。     

基于组合方式的延时精确测量方法

研究了延时线的精确时间测量方法。首先介绍了传统群时延测量方法的误差，并分析了误差形成的机理，然后提出采用基于群时延和相位时延相结合的方式进行延时精确测量的方法。采用二次测量的方式，结合群时延的粗测量和相位时延的精测量，完成延时的精确测量，并通过仿真、试验验证了该方法在延时测量方面的精度更高。     

星箭分离减冲击环结构优化设计

针对星箭分离过程，建立了减冲击环结构模型，并分析了该结构的减冲原理。利用冲击响应谱的时域合成算法计算获得符合冲击谱规范的时域载荷，并以此作为结构优化设计中动力学模型的输入载荷工况。以减冲击环的连续层数、单层支撑块数、连续层厚度、支撑块层高度以及环的外半径和内半径6个结构参数为设计变量，以减冲效果和结构强度、刚度为约束，以质量最轻为目标，建立减冲击环的结构优化设计模型，这是一包含离散变量和连续变量的混合优化问题。为了在跨平台下求解该混合优化问题，采用代理模型策略和自适应模拟退火算法求解，并在最优点处开展结构性能对设计变量的敏度计算，对计算结果进行了分析评估。本研究对强冲击动力学环境下的航天器结构分析与设计有一定的指导意义。     

Enhanced ultrasonic total focusing imaging of CFRP corner with ray theory-based homogenization technique

Ultrasonic testing is effective in defect characterization and quality assurance of Carbon Fiber Reinforced Plastic (CFRP) components in the aerospace industry. Due to the coupling between complex shape and elastic anisotropy, the Phased Array Ultrasonic Testing (PAUT) and time-based Total Focusing Method (TFM) face significant challenges in the calculation of wave propagation. A wave velocity distribution model is established for a multidirectional convex corner of CFRP based on a homogenization theory and the above coupling effects are also incorporated. A ray-tracing method is proposed based on Dijkstra’s shortest path search algorithm. The predicted time of flight ensures that this technique, the homogenized TFM, could synthesize a high-quality focused image by post-processing on the full matrix capture data. Experiments on a laminate with three ?1.5 mm Side-Drilled Holes (SDHs) in different circumferential directions confirm a successful homogenized TFM imaging that all SDHs can be effectively detected. As compared to the isotropic scenario, the maximum positioning error is reduced to 0.12, 0.08, and 0.38 mm, and the Signal-to-Noise Ratios (SNRs) are increased by 2.1, 1.1, and 11.8 dB, respectively. It is suggested that the ray-tracing assisted TFM technique can effectively improve the imaging of corners in CFRP components.     

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.     

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.