天琴计划轨道构型长期漂移特性分析

为了实现天琴计划航天器轨道构型的长期稳定漂移并满足轨道构型的稳定性指标,文章应用优化算法搜索航天器初始最优瞬时轨道要素。建立考虑摄动情况下航天器轨道动力学模型,对导致等边三角形轨道构型发生长周期漂移的轨道要素进行了分析,并且将这些要素作为轨道设计的待优化参量,应用粒子群（ParticleSwarmOptimization,PSO）智能寻优算法进行优化,应用最终优化得到的轨道要素进行轨道构型仿真。仿真结果表明,在不进行轨道控制的情况下,4年内轨道构型呼吸角变化小于1.5°,臂长变化小于1500km,臂长变化速率小于10km/s,初步满足轨道构型的稳定性指标。     

基于粒子群算法的多脉冲转移轨迹优化

通过引入Lambert算法处理终端约束条件,建立基于可行解迭代的多脉冲转移轨迹优化模型,采用粒子群算法优化最省燃料转移轨道,并对分别采用变轨点真近点角和变轨时刻作为设计变量的优化结果进行了对比分析.对相同的两脉冲、三脉冲轨道转移问题,优化结果验证了提出的优化模型和优化算法的正确高效性.仿真表明,使用变轨点真近点角为设计变量时优化效率和结果更好.     

Using gamma-ray burst prompt emission to probe relativistic shock acceleration

It is widely accepted that the prompt transient signal in the 10 keV–10 GeV band from gamma-ray bursts (GRBs) arises from multiple shocks internal to the ultra-relativistic expansion. The detailed understanding of the dissipation and accompanying acceleration at these shocks is a currently topical subject. This paper explores the relationship between GRB prompt emission spectra and the electron (or ion) acceleration properties at the relativistic shocks that pertain to GRB models. The focus is on the array of possible high-energy power-law indices in accelerated populations, highlighting how spectra above 1 MeV can probe the field obliquity in GRB internal shocks, and the character of hydromagnetic turbulence in their environs. It is emphasized that diffusive shock acceleration theory generates no canonical spectrum at relativistic MHD discontinuities. This diversity is commensurate with the significant range of spectral indices discerned in prompt burst emission. Such system diagnostics are now being enhanced by the broad-band spectral coverage of bursts by the Fermi Gamma-Ray Space Telescope; while the Gamma-Ray Burst Monitor (GBM) provides key diagnostics on the lower energy portions of the particle population, the focus here is on constraints in the non-thermal, power-law regime of the particle distribution that are provided by the Large Area Telescope (LAT).     

Non-stationarity and cross-correlation effects in the MHD solar activity

The analysis of turbulent processes in sunspots and pores which are self-organizing long-lived magnetic structures is a complicated and not yet solved problem. The present work focuses on studying such magneto-hydrodynamic (MHD) formations on the basis of flicker-noise spectroscopy using a new method of multi-parametric analysis. The non-stationarity and cross-correlation effects taking place in solar activity dynamics are considered. The calculated maximum values of non-stationarity factor may become precursors of significant restructuring in solar magnetic activity. The introduced cross-correlation functions enable us to judge synchronization effects between the signals of various solar activity indicators registered simultaneously.     

Bayesian coronal seismology

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.     

基于小波变换和支持向量机的开关电流电路故障诊断新方法

针对开关电流(Switched current,SI)电路的故障诊断和定位问题,为进一步提高故障诊断准 确率,提出了基于小波变换和粒子群优化(Particle swarm optimization,PSO)支持向量机 (Support vector machine,SVM)的开关电流电路故障诊断新方法。该方法首先对节点电流 信号进行蒙特卡罗分析,然后通过小波分解计算分形维数,再利用核主元分析（Kernel pri ncipal component analysis,KPCA）降低特征值维数,实现最优故障特征的提取。最后通 过PSO SVM完成对各种故障模式的分类。对六阶切比雪夫低通滤波器进行了仿真实验验证, 获取了较高的故障诊断准确率,与其他方法进行比较,实验结果显示了本文方法的优越性。     

基于卷积盲分离的强干扰下通信信号分离算法

针对目前通信辐射源个体识别算法在实际试验中由于各类干扰信号和多径衰落导致识别率较低的问题,提出一种用于识别算法前端的信号分离算法,可有效地减少其他电磁信号对于识别算法输入信号的影响,从而提高在复杂电磁环境中通信辐射源个体识别的正确识别率。该算法将灾变策略和搜索状态的自适应引入量子粒子群算法,通过对混合信号的联合对角化从截获的观测信号中提取出目标通信辐射源的有用信号。为了更加系统、直观地衡量算法的分离效果,提出分离熵来量化算法的整体性能。仿真结果表明,该分离算法可以把目标通信辐射源的有用信号从复杂电磁环境中提取出来,从而提高通信辐射源个体识别在复杂电磁环境中的正确识别率,具有较好的可行性和有效性。     

基于自适应阈值的粒子滤波非线性系统故障诊断

针对实际非线性系统传统方法难以实现可靠故障诊断的问题,采用粒子滤波算法,利用对数似然函数和作为评价指标,并借助自适应阈值的设计,研究了非线性非高斯系统的故障检测和故障隔离。阈值的选取,是准确判别系统是否发生故障的标准,本文通过分析非线性系统残差的统计特性,在判定残差统计特性成正态分布的基础上,设计了一种基于粒子滤波故障诊断的自适应阈值方法,减少了故障诊断的漏报和误报。通过非恒温连续搅拌水箱式反应堆的仿真实例,验证了该方法在故障诊断中的准确性和有效性。      

小型无人机气动参数辨识的新型HGAPSO算法

针对小型无人机(UAVs)研制中操稳特性和飞行控制律设计评估对气动参数辨识的需求,提出了一种混合遗传粒子群优化算法(HGAPSO)。该算法以粒子群优化算法(PSO)为主体,在粒子优化路径中,引入遗传算法(GA)的交叉变异操作,增强粒子群跳出局部最优的能力;同时采用Kent映射改进粒子种群的初始化,使初始种群在可行解空间内分布更加均匀,增强全局优化能力。基于仿真结果,依据辨识准度及辨识成功率,对比了HGAPSO、常规PSO和GA优化算法气动参数辨识的结果,然后用蒙特卡洛仿真测试随机观测噪声的影响,结果表明该算法兼备PSO算法高的搜索效率和GA算法的全局优化能力,对随机观测噪声不敏感。最后,通过设计小型UAV试飞示例进行综合应用评价,结果表明:HGAPSO算法基于真实试飞数据进行气动参数辨识取得了满意结果,具有良好的实用性。