Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

基于单液滴蒸发的离心喷雾干燥数值模拟

为了分析离心式喷雾干燥塔内物理场分布和液滴运动规律,采用欧拉-拉格朗日法,耦合单液滴干燥模型,建立了喷雾干燥过程的三维稳态数学模型。以70%(湿基)麦芽糊精溶液为原料液,在中试喷雾干燥系统上进行实验,测定了塔内温度分布。结果表明,模拟的塔内特征点温度与实验结果吻合良好,平均相对误差为1.84%,验证了模拟结果的准确性和可靠性。考察塔内连续相传递过程发现,三维物理场分布不是轴对称的,塔中心区域具有较高的温度、较低的水蒸气含量和较大的气流速度。分析液滴运动轨迹和干燥过程发现,较大的液滴干燥时间较长,较小的液滴干燥时间短但由于发生回旋返混停留时间较长。模拟研究了进风温度、雾化盘转速和进风角度对塔内物理场分布和液滴干燥行为的影响,分析了喷雾干燥过程的传质传热机理。

基于迁移学习的高速公路交织区车辆轨迹预测

高速公路交织区复杂场景下的车辆轨迹预测对智能汽车的决策与控制具有重要意义.为应对交织区复杂交通流带来的轨迹预测实时性与精确性等挑战,提出一种基于迁移学习的车辆轨迹预测方法,利用已有的高速公路直线段轨迹预测模型进行迁移学习训练,从而实现在交织区场景中更快速精准地轨迹预测.使用NGSIM(next generation simulation)数据集中的交织区轨迹数据,采用长短时记忆神经网络模型,在已充分训练的高速公路直线段模型基础上对交织区进行迁移学习,并采用时间序列滚动预测法逐帧精准预测轨迹.实验结果表明,横向和纵向行为预测准确率可达98.35%和93.01%,轨迹预测值的均方根误差为2.04 cm.交织区迁移学习能够缩短61.1%的模型训练时间,同时提高预测准确率和模型泛化能力.

可控电感在WPT系统动态性能的优化方法

SS型Buck-WPT(Buck-wireless power transfer)系统由Buck电路和基本的SS型无线电能传输电路组成。该电路系统因为结构和控制方式简单、控制效果明显等优点在感应式无线电能传输方面得到广泛应用。但该电路的动态特性并不能满足一些时变系统对快速性的较高要求。例如,系统在启动时会存在较强震荡和较大超调,系统负载改变时稳定状态会发生改变且存在明显抖动,系统极限空载时原边谐振电流会增大,且该电流值远超出安全工作范围。本文提出了一种基于可控电感的SS型Buck-WPT系统。首先,分析了电感值可调的方法并在COMSOL中建立仿真模型验证其电感值可控的特性。其次,对SS型Buck-WPT系统进行数学建模,将SS型WPT系统作为Buck电路的特殊负载,推导SS型Buck-WPT系统状态空间方程。研究其三维空间内相轨迹的降维描述方法,将该系统用二维相轨迹描述系统运行过程。然后,通过分析启动阶段相轨迹运行规律,改进前级Buck电路。将传统Buck电路中的电感换成可控电感,运用其电感值可调的控制系统开通阶段的运行轨迹,使系统在1个开关周期内无超调快速进入稳态。当系统负载改变时,系统的输出电压会改变,且是不断抖动来回反复的过程,利用PI算法对系统进行恒流控制。通过可控电感控制系统相轨迹,使副边输出能无抖动快速进入稳态,保证输出电压不变。针对SS型谐振网络的Buck-WPT系统中出现空载大电流的问题,提出了将可控电感串联接入原边谐振网络的方法。实时检测原边谐振电流值,该值超过正常工作范围,感值就快速增大,减小原边谐振电流,达到空载时维持原边谐振电流安全值以下。最后,验证上述方法在优化SS型Buck-WPT系统动态特性的有效性,在Simulink中搭建仿真电路。该方法能减小工作条件改变时带来的系统抖动,且在不改变系统响应速度前提下减小超调,优化系统动态性能,增强系统抗负载扰动力,提高系统带负载能力有明显效果。

1990—2020年黄河流域(陕西段)土地利用格局时空演变分析

黄河流域是我国最重要的生态屏障之一,承担着重要的生态调节功能,一直以来都是自然环境与人文活动交互作用强烈的地区。以黄河流域(陕西段)为研究区域,基于多源数据,对其土地利用格局时空演变、重心迁移情况进行分析,并采用PLUS模型对2030年土地利用进行模拟预测。结果表明:1)1990—2020年黄河流域(陕西段)土地利用类型变化主要是旱地、林地、草地之间的相互转化,也存在与其他地类间的相互流动。2)研究期内黄河流域(陕西段)各土地利用类型重心迁移轨迹具有明显的方向性,且过程中普遍存在往复和折返的运动轨迹。3)1990—2020年黄河流域(陕西段)不同功能土地利用类型间存在一定的分布和转化规律,关中地区生活建设用地扩张明显,陕北地区以生态保育用地和粮食生产用地相互转化为主。4)预测结果显示,未来10年黄河流域(陕西段)仍会以城镇化建设为重;林草地等生态价值明显的地类面积下降幅度不大。

Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

Ballistic Trajectory Extrapolation and Correction of Firing Precision for Multiple Launch Rocket System

The research on multiple launch rocket system(MLRS)is now even more demanding in terms of reducing the time for dynamic calculations and improving the firing accuracy,keeping the cost as low as possible.This study employs multibody system transfer matrix method(MSTMM),to model MLRS.The use of this method provides effective and fast calculations of dynamic characteristics,initial disturbance and firing accuracy.Further,a new method of rapid extrapolation of ballistic trajectory of MLRS is proposed by using the position information of radar tests.That extrapolation point is then simulated and compared with the actual results,which demonstrates a good agreement.The closed?loop fire correction method is used to improve the firing accuracy of MLRS at low cost.

The Simulation and the Calculation of the Shortest Hohmann Transfer Orbit to Mars

This paper will present the results and analyses of a simulation to send a satellite from the Earth to Mars. We use Python to simulate the orbit of the rocket. Our goal is to find the least energy-cost trajectory, with the least initial velocity. We find the date which allows the satellite to go from the Earth to Mars in the shortest distance based on a Hohmann transfer orbit considering the gravity of the Sun, Earth, and Mars.

基于混合自动机的综合能源系统状态转移空间建模

目前对于综合能源系统的建模,更多从系统输入-输出关系、内部状态与输入输出的关系进行描述,其内部状态在不同阶段的转移过程难以清晰表达。在分布式可再生能源占比大幅度提升后,综合能源系统在不同阶段的运行状态及状态转移过程需要可观可控。为此,提出了基于混合自动机的综合能源系统分阶段状态转移空间建模方法,对综合能源系统内部状态的分阶段转移条件及其转移过程进行精准描述,有利于对综合能源系统内部所有能源生产单元和能源消费单元从启动、运行到停止的全过程进行分阶段管理与优化控制。仿真算例表明,所提模型对综合能源系统分阶段的状态转移轨迹实现了全过程观测,有利于实现全过程分阶段的高效管理与优化控制。

基于近邻算法的无人机采集森林数据航迹路线的规划

为提升数据采集效率,提出基于近邻算法的无人机数据采集航迹规划算法(Nearest Neighbor-based trajectory planning of UAVs for Data Collection, NNDC)。该算法允许无人机通过无线电能传输(Wireless Power Transfer, WPT)向节点补给能量,延长节点的工作时间。以无人机能耗和确保收集每个节点数据为约束条件,建立最小化数据采集时间的目标函数,利用近邻算法求解目标函数,获取无人机最优的数据采集航迹,实现对无人机航迹的规划。性能分析表明,相比于同类算法,NNDC算法降低了数据采集时间和数据采集阶段的无人机能耗。在节点数从6~20变化期间,NNDC算法采集所有节点数据的平均时间低于100 s。

珠江流域旱涝急转灾害时空演变趋势分析

珠江流域作为典型的亚热带湿润区,其气候条件受气旋、副热带高压等大气系统交替和流域复杂下垫面条件的影响,极易发生旱涝急转灾害,严重威胁珠江流域的水安全与粮食安全。为了全面分析珠江流域旱涝急转灾害时空演变趋势,基于1961—2020年珠江流域实测水文气象数据,采用趋势检验、空间重心转移轨迹等方法对珠江流域旱涝急转事件的烈度、历时、频次和强度等属性特征进行分析。结果显示虽然珠江流域旱涝急转事件的整体年际变化趋势不显著,但近20 a(2001—2020年)珠江流域旱涝急转事件相较于1961—2000年有明显减缓;空间上,珠江流域涝转旱事件主要集中于流域上游地区,中游地区受涝转旱影响较轻;1961—2020年,涝转旱事件各个属性特征值重心逐步向西江流域上游方向转移,旱转涝事件各个属性特征值则具有向流域下游方向转移的趋势。

Perturbed low-thrust geostationary orbit transfer guidance via polynomial costate estimation

This paper proposes an optimal,robust,and efficient guidance scheme for the perturbed minimum-time low-thrust transfer toward the geostationary orbit.The Earth’s oblateness perturbation and shadow are taken into account.It is difficult for a Lyapunov-based or trajectory-tracking guidance method to possess multiple characteristics at the same time,including high guidance optimality,robustness,and onboard computational efficiency.In this work,a concise relationship between the minimum-time transfer problem with orbital averaging and its optimal solution is identified,which reveals that the five averaged initial costates that dominate the optimal thrust direction can be approximately determined by only four initial modified equinoctial orbit elements after a coordinate transformation.Based on this relationship,the optimal averaged trajectories constituting the training dataset are randomly generated around a nominal averaged trajectory.Five polynomial regression models are trained on the training dataset and are regarded as the costate estimators.In the transfer,the spacecraft can obtain the real-time approximate optimal thrust direction by combining the costate estimations provided by the estimators with the current state at any time.Moreover,all these computations onboard are analytical.The simulation results show that the proposed guidance scheme possesses extremely high guidance optimality,robustness,and onboard computational efficiency.

基于Gauss伪谱法的高空飞行器再入段轨迹优化对传热效应的影响分析

基于Gauss伪谱法和二阶有限差分(Gauss Pseudospectral Method,GPM),研究了再入过程中高速飞行器的传热问题,并依据最内温层升温最小的目的进行飞行轨迹的数值优化。主要思路:构造传热分析模型,并依据传热方程构造每一温层的传热微分方程;将各温层微分方程以及动力学微分方程作为伪谱法中的微分方程约束条件代入,进行轨迹数值优化设计;利用少量LG点构造拉格朗日多项式,再通过一维插值获得大量LG点的值,获得更高精度的拟合结果曲线。以某高超声速飞行器为对象用本方法进行数值计算,结果验证了方法具有一定的可行性。

限电弃风工况下双馈风电机组有功及调频控制策略

双馈风电机组限电弃风运行能够为电网提供备用和频率支撑。为减小限电弃风工况下转速和桨距角频繁动作致使机组产生的机械疲劳,提出一种基于机组运行点转移轨迹优化的有功控制策略,并通过引入下垂控制使其具备了参与电网一次调频的功能。对限电弃风工况下机组在不同运行点处的稳定性进行分析;考虑机组有功平衡及运行点稳定性等约束条件,建立以转速和桨距角综合调整量最小为目标函数的运行点转移轨迹优化模型,并提出优化模型在出现不可行解时的处理机制,以获得可行的最优转移轨迹;建立机组运行轨迹和调频控制器,并对变桨系统和转子侧变频器的传统控制策略进行改进,使机组能够沿最优转移轨迹调整有功和参与调频。仿真结果表明,利用所提控制策略能使机组在准确完成限电弃风任务并为电网提供频率支撑的同时,大大减少机组转速和桨距角的调整量。

嫦娥五号平动点拓展任务轨道方案研究

中国嫦娥五号探测器成功实现月球采样返回任务,为最大限度利用任务资源,研究了利用嫦娥五号轨道器的平动点拓展任务轨道方案,设计了平动点轨道及其转移轨道。首先,给出了任务轨道设计的轨道动力学模型,包括圆型限制性三体问题模型和精确力模型。其次,基于嫦娥二号和嫦娥5T1平动点拓展任务设计经验,介绍了平动点轨道直接转移与入轨等轨道设计方法。最后,基于嫦娥五号轨道器与返回器分离后的状态,设计了包括日地Lissajous轨道、地月Lyapunov轨道和NRHO轨道的平动点拓展任务方案,其中日地Lissajous轨道方案分别考虑了轨道器规避机动至不同近地点高度的情况,地月Lyapunov轨道方案采用月球借力转移方式并且涵盖了地月L1和L2点,NRHO轨道方案包括直接深空机动转移和经日地L1点转移的方式。研究结果表明,拓展任务方案所需速度增量不超过400 m/s,最小为103 m/s,转移飞行时间基本不超过90天,最短为70天,研究方案可为嫦娥五号轨道器拓展飞行任务提供有益参考,实现了我国首次日地L1点探测飞行。

伊犁河谷一次极端强降水事件水汽特征分析

2016年7月31日至8月1日,新疆伊犁河谷发生了一次极端强降水事件,多站突破降水极值。利用NCEP/NCAR 1°×1°和2.5°×2.5°再分析资料、中国地面卫星雷达三源融合逐小时降水产品、新疆地区常规观测资料、基于地基GPS观测的大气可降水量资料及基于拉格朗日方法的HYSPLIT轨迹模式结果,通过对水汽输送流函数、势函数、水汽输送轨迹和暴雨区水汽收支计算,结合伊犁河谷GPS观测分析,揭示了此次强降水期间的大尺度水汽输送、辐合特征及伊犁河谷局地水汽变化特点。结果表明:(1)强降水期间大西洋及红海均对伊犁河谷的水汽供应具有贡献,河谷处于水汽通量辐合区,向西开口的地形辐合和抬升为局地暴雨的发生提供有利的动力辐合条件。低纬度印度夏季风环流和中纬度大西洋向东输送的气流共同构成伊犁河谷极端降水天气的水汽输送通道,其中印度夏季风西南水汽输送主要集中在对流层低层,对流层中层水汽的输送以大西洋向东气流和低槽自身水汽输送为主。(2)HYSPLIT模拟结果表明暴雨区3000 m中纬度偏西路径的水汽输送最为强盛,偏南路径水汽源于阿拉伯海,对流层底层偏西、偏东路径和中层偏北路径水汽通过垂直运动补充对流层低层的水汽;5000 m水汽输送轨迹以偏西路径和低槽自身携带的水汽为主。(3)降水期间水汽集中在对流层低层,通过垂直输送项向高层输送;强降水时段暴雨区对流层低层南边界水汽流入量迅速增强,中高层水汽流入主要集中在西边界。(4)降水前槽前西南气流造成伊犁河谷测站GPS-PWV明显跃升,强降水时段受印度西南季风影响,测站PWV快速增高并维持,局地GPS-PWV的增加与大尺度水汽输送辐合增强有关。

“嫦娥二号”探测小行星任务转移轨道设计

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