基于粒子群优化核极限学习机的北斗超快速钟差预报

针对卫星钟差序列中非线性特性较为复杂和超快速钟差预报精度较低的问题,将核极限学习机算法引入到北斗超快速钟差预报中。首先,将极限学习机进行优化,引入粒子群优化算法来选择核极限学习机所需的核参数和正则化参数;然后,将优化后的方法应用到超快速钟差预报中,并给出了利用该方法进行超快速钟差预报的步骤;最后,在分析iGMAS提供的实测北斗超快速钟差数据的基础上,选用单天和多天数据进行短期预报。结果表明：在短期预报6h范围内,利用本文提供的优化方法解算得到的超快速钟差预报精度明显优于二次多项式模型和周期项模型,并且采用此方法得到的超快速钟差预报产品与iGMAS提供的超快速钟差预报产品(ISU-P)相比,GEO、IGSO和MEO卫星的预报精度分别提升了50.51%、46.98%、40.67%,其与最终精密钟差的符合程度显著 增强 。     

改进的迭代粒子滤波法在GPS数据处理中的应用

研究粒子滤波方法(PF)处理GPS测量数据.采用迭代Kalman滤波方法产生粒子滤波器中的粒子,得到更精确的目标状态估计.同时利用目标运动方程向后外推目标状态,综合利用外推的目标状态以及历史现测数据确定各粒子的权值,降低现测随机误差的影响,从而提高粒子滤波器的性能.采用改进的粒子滤波方法处理GPS测量数据,仿真结果表明...     

基于贝叶斯方法的HPRF雷达跟踪解距离模糊方法(英文)

高脉冲重复频率(HPRF)雷达目标跟踪需要解决距离模糊问题。本文通过把脉冲间隔数和脉冲间隔变化量作为目标的离散状态,目标的距离和径向速度作为目标的连续状态,将HPRF雷达的跟踪和解距离模糊问题转换为贝叶斯框架下的混合估计问题并且利用粒子滤波方法实现。仿真结果表明本文方法在脉冲间隔数变化时仍可以正确地解距离模糊,并且跟踪精度比多假设方法高。     

颗粒级配优化研究——滚动级配法

提出了一个颗粒级配优化新理论——滚动级配法。揭示了宽广级配范围内,在相同配方系和相等颗粒比表面条件下粘度最低的级配规律。用这一理论指导配方设计时,不仅药浆工艺性能最好而且有较宽的配方“自由度”以满足弹道和力学性能的要求。本研究全部实验结果表明:凡是符合滚动级配法的推进剂配方,其药浆粘度最低;反之,粘度就大。认为“最紧密排列理论”是滚动级配法的特例。     

一种基于Particle Filter的攻击目标高度估计算法

利用目标高度估计确定目标攻击要害点是精确制导武器信息处理中的一个重要内容。传统方法主要有直接利用几何方法估计和扩展卡尔曼滤波器方法,这两种方法精度都不高。Partic le F ilter是一种新出现的滤波方法,在解决非线性问题中得到了广泛应用。利用Partic le F ilter设计了一种新的目标高度估计算法。该算法通过贝叶斯递推方法,避免了在测量方程非线性很强的时候,扩展卡尔曼滤波器不合理的线性化所带来的误差。仿真结果表明,这种基于Partic le F ilter的目标高度估计算法提高了估计精度和收敛的鲁棒性。     

铝粉颗粒燃烧及其燃烧产物凝聚成核计算研究

在建立铝颗粒燃烧模型及其燃烧产物凝聚成核模型基础上，对铝粉燃烧所形成的Ａｌ２Ｏ３平均粒径了进行了计算研究。也对推进剂燃烧环境下铝颗粒燃烧区域中温度、燃烧产物的质量分数、过饱和度及其凝聚成核速度的变化规律进行了分析讨论。     

RJMCMC粒子滤波方法在月球探测器自主天文导航中的应用

天文导航系统是典型的非线性和噪声非高斯分布的系统。针对传统的扩展卡尔曼滤波不适于非线性和噪声非高斯分布的系统，和一般粒子滤波存在的粒子退化等问题，提出了一种将RJMCMC（可逆跳转马尔可夫链蒙特卡罗）算法应用于月球探测器自主天文导航粒子滤波器中的新方法。计算机仿真结果显示了该方法在加快收敛速度、提高导航定位精度和自适应调整粒子个数方面的有效性和可行性。     

Relative ion Fe,C and O abundances in quiet time particle fluxes in the 23 SC

Using ACE and SOHO data the origin of quiet-time low-energy particle fluxes at 1 AU is studied in the 23rd solar cycle. One of the selection criteria of quiet-time periods is to demand that H/He < 10 provided that periods with noticeable contribution of remnants of gradual events have been excluded from consideration. Our results suggest different origin of 0.03–3 MeV/nucleon particles – different seed populations accelerated and different acceleration processes. During the ascending, maximum and descending phases of solar activity quiet-time ions consist of coronal particles accelerated to suprathermal energies in about a half of the quiet periods, the rest of quiet-time fluxes originates from particle acceleration in processes similar to those in small impulsive solar flares rich in Fe. At solar minimum the bulk solar wind particles serve as seed population.     

Alfvén waves as a driving mechanism in stellar winds

Alfvén waves have been invoked as an important mechanism of particle acceleration in stellar winds of cool stars. After their identification in the solar wind they started to be studied in winds of stars located in different regions of the HR diagram. We discuss here some characteristics of these waves and we present a direct application in the acceleration of late-type stellar winds.     

Adaptive Square-Root Cubature–Quadrature Kalman Particle Filter for satellite attitude determination using vector observations

A novel algorithm is presented in this study for estimation of spacecraft?s attitudes and angular rates from vector observations. In this regard, a new cubature–quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature–Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.