基于FTF算法的自适应谱线增强器在罗兰-C信号处理中的应用

针对最小均方误差(LMS)算法的自适应谱线增强器收敛速度慢的缺点,将快速横向滤波(FTF)算法的自适应谱线增强器应用于罗兰-C信号中随机噪声的抑制。经过仿真验证,结果表明,FTF算法自适应谱线增强器具有收敛速度快,输出均方误差小的优点,能够克服LMS算法的不足,用来抑制罗兰-C信号中的随机噪声是可行的。     

一种时间尺度算法的稳定度分析

介绍了国际权度局时间频率分部的ALGOS原子时算法的原理，并提出了建立在小波分解基础上的一种新的时间尺度算法，它可以提取原子钟在不同频率范围内的不同稳定度，实验结果表明：根据此算法建立的小波分解原子时稳定度优于一般的算法。     

Analytical design methods for transfer trajectories between the Earth and the Lunar Orbital Station

Lunar Orbital Station (LOS) is proposed as support of manned lunar exploration missions. A fast-converging iteration method for determining the initial conditions of two-impulse transfer trajectories between the Earth and the LOS is proposed based on the patched conic approach. In the Earth phase, near Earth state is connected with the state at the lunar sphere of influence (LSOI) based on the relationship between the initial and terminal orbital state. Then, an analytical algorithm is proposed to find the state vector at LSOI, such to satisfy the LOS orbital constraint. An iterative process is finally adopted to generate favorable initial solutions that satisfy the constraint near the Earth and at the perilune. The algorithm convergence is investigated, and two types of transfer trajectories are found for both Earth-LOS and LOS-Earth transfer. Based on the algorithm, orbital transfer windows, velocity impulse and time of flight are analyzed in the typical years 2025 and 2034. At last, the initial solution is corrected with a high fidelity model based on the active-set method, which shows the precision of this algorithm. The novel procedure for the transfer trajectories design and the analytic result can be used as a basis for rapid mission evaluation and design for future manned lunar missions based on the LOS.     

Maximum entropy restoration of noisy star images

We employ Friden's algorithm based on the maximum likelihood principle to restore one-dimensional star image data in the presence of noise. The algorithm is made iterative and its performance discussed.     

原子时的小波分解算法

时间尺度的就是根据一组原子钟的数据,用统计的方法计算出平均的时间尺度。其目的是使综合时间尺度的噪声最小。一般的原子时算有脱离经典加权的局限性,只能抑制原子钟的栽一种噪声。运用小波分解的方法建立以分解原子时,将原子信号在小汉域按频率尺度分解原,然后在不同频率范围加权,这种方法不但考虑不同原子钟在同一个频率分量的不同稳定度,而且考虑不同频率分量的不同稳定度,它有着独特的优越性。     

一种基于短时傅立叶变换的脉冲星消色散算法

叙述了一种基于短时傅立叶变换的脉冲星消色散算法。首先介绍了脉冲星的色散形成原因以及几种当今流行的消色散算法。接着介绍了短时傅立叶变换,并在此基础上提出了一种基于短时傅立叶变换的消色散算法。然后详细讲述了这种消色散算法的具体实现步骤,并且比较了在选取不同长度的时间窗函数的情况下,消色散的处理过程和残余色散量。最后,通过将这种算法的计算量以及结果和其他几种算法做横向比较,得出了结论:这种消色散算法的计算量小,实现简单,可以有效的进行消色散处理。     

一种固定选图数的实时幸运成像算法

幸运成像技术是一种从大量短曝光图像中选取少量幸运好图进行配准、叠加的高分辨率图像恢复技术,能够有效减小大气湍流对图像质量的影响,但传统的基于中央处理器(Central Processing Unit,CPU)的幸运成像算法难以实现实时化.利用现场可编程门阵列(Field Programmable Gate Array,...     

Particle swarm optimization based space debris surveillance network scheduling

The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.     

Detrending time series for astronomical variability surveys

We present a detrending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, changes of airmass, telescope vibration, CCD noise or defects of photometry. Those trends undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among themselves. These subsets are selected based on a hierarchical tree clustering algorithm. A bottom-up merging algorithm based on the departure from normal distribution in the correlation is developed to identify subsets, which we call clusters. After identification of clusters, we determine a trend per cluster by weighted sum of normalized light curves. We then use quadratic programming to detrend all individual light curves based on these determined trends. Experimental results with synthetic light curves containing artificial trends and events are presented. Results from other detrending methods are also compared. The developed algorithm can be applied to time series for trend removal in both narrow and wide field astronomy.     

A fuzzy logic‐based algorithm for cosmic‐ray hit rejection from single images

An algorithm for cosmic‐ray rejection from single images is presented. The algorithm is based on modeling human perception using fuzzy logic. The proposed algorithm is specifically designed to reject multiple‐pixel cosmic ray hits that are larger than some of the point spread functions of the true astronomical sources. Experiments show that the algorithm can accurately reject ∼97.5% of the cosmic rays hits, while mistakenly rejecting 0.02% of the true astronomical sources. The major advantage of the presented algorithm is its computational efficiency. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

初轨的稳健估计算法

讨论单测角资料(数据中包含异常值)初轨的稳健估计算法．该算法既可用于提供可靠的初轨,又可用作资料预处理,剔除异常值．稳健估计算法的崩溃点为16％至25％,可剔除6σ以上的异常值．     

An Orbital Prediction Algorithm for LEO Satellites Based on Optical Observations of Short Arcs at Single Station

The forecasting technique of the target tracking based on the short arcs at single station is an important way to guarantee that high-precision photoelectric theodolites can normally track and capture the targets in unconventional environments. We construct the tracking prediction algorithm based on nonlinear filter, which can provide the guiding data for the closed loop tracking under normal circumstances. At the same time we also construct the target prediction algorithm based on the nonlinear transformation, without valid observational data, which can provide a track guidance for the theodolite and ensure that the targets will not be lost. It is demonstrated that the nonlinear filtering is more effective than the EKF (extended Kalman filter) in the tracking prediction algorithm of the short arcs at single station. The results indicate that the nonlinear filter designed in this paper can be used as the guiding algorithm for the optical tracking equipments. And its guiding accuracy is in the same order of magnitude of the theodolite's random measurement accuracy. When the systematic error of the equipments reaches 50″, the accuracy can achieve 20″ for predictions in 60 s. This still satisfies the requirement of the field of view of the tracking equipments.     

近距离罗兰-C信号周期识别的数字化方法初探

对判断罗兰-C脉冲峰值的算法作了改进。对加有噪声的近距离罗兰- C脉冲仿真信号的采样值用改进的算法进行计算后,可识别信号周期并给出相位编码信息。对改进后的算法的有效性用MATLAB进行了验证,并用MODELSIM对改进的算法进行了仿真实现。结果表明：在一定的信噪比条件下,改进后的算法初步实现了对近距离罗兰-C信号周期识别的数字化,具有一定的实用性。     

基于小波方法的平滑算法在引力透镜中的应用

基于离散小波变换(DWT)方法,提出了一种可用于计算三维数值模拟样本面密度的平滑算法.为检验方法的有效性,利用该算法研究了两组不同质量解析度的引力透镜数值模拟样本,样本采用了暗物质晕的等温椭球模型,使用蒙特卡罗方法生成.计算结果表明此算法能够在很高的精度上构建引力透镜模拟样本的面密度分布轮廓,由面密度计算出来的透镜的临界曲线和焦散曲线也能较好地和理论曲线吻合,结果是令人满意的.同时比较了三组不同的小波基的计算结果,包括Daub4,Daub6和B-spline 3th,给出了最优的选择.在不损失平滑效果的同时,此算法具有非常高的速度,非常适合于处理以后更高精度的N体数值模拟.     

Stochastic parallel gradient descent based adaptive optics used for a high contrast imaging coronagraph

An adaptive optics(AO) system based on a stochastic parallel gradient descent(SPGD) algorithm is proposed to reduce the speckle noises in the optical system of a stellar coronagraph in order to further improve the contrast. The principle of the SPGD algorithm is described briey and a metric suitable for point source imaging optimization is given. The feasibility and good performance of the SPGD algorithm is demonstrated by an experimental system featured with a 140-actuator deformable mirror and a Hartmann-...     

The Cannon: A Data Driving Approach to Stellar Label Determination

The new generation of large sky area spectroscopic survey project has produced nearly 10 million low-resolution stellar spectra. Based on these spectroscopic data, this paper introduces a machine learning algorithm named The Cannon. This algorithm is completely based on the known spectroscopic data of stellar atmospheric parameters (effective temperature, surface gravity, and metal abundance, etc.), this algorithm builds the characteristic vector by means of data driving, and establishes the functional relation between spectral flux characteristics and stellar parameters. Then it is applied to the observed spectral data to calculate the atmospheric parameters. The main advantage of The Cannon is that it is not directly based on any stellar physical models, it has an even higher applicability. Moreover, because of the use of full-spectrum information, even for the spectra with a low signal-to-noise ratio (SNR), it still can obtain the parameter solutions of high reliability. This algorithm has significant advantages in the data processing and parameter determination of large-scale stellar spectra. In addition, this paper presents two examples of using The Cannon to obtain the stellar parameters of K and M giants from the LAMOST spectral data.     

Meteor shower detection with density‐based clustering

We present a new method to detect meteor showers using the density‐based spatial clustering of applications with noise algorithm (DBSCAN; Ester et al. 1996 ). The DBSCAN algorithm is a modern cluster detection algorithm that is well suited to the problem of extracting meteor showers from all‐sky camera data because of its ability to efficiently extract clusters of different shapes and sizes from large data sets. We apply this shower detection algorithm on a data set that contains 25,885 meteor trajectories and orbits obtained from the NASA All‐Sky Fireball Network and the Southern Ontario Meteor Network (SOMN). Using a distance metric based on solar longitude, geocentric velocity, and Sun‐centered ecliptic radiant, we find 25 strong cluster detections and six weak detections in the data, all of which are good matches to known showers. We include measurement errors in our analysis to quantify the reliability of cluster occurrence and the probability that each meteor belongs to a given cluster. We validate our method through false‐positive/negative analysis and with a comparison to an established shower detection algorithm.     

Fast Object Detection for Use Onboard Satellites

We propose an object detection algorithm which is efficient and fast enough to be used in (almost) real time with the limited computer capacities onboard satellites. For stars below the saturation limit of the CCD detectors it is based on a four neighbourhood local maximum criterion in order to find the centre of a stellar image. For saturated stars it is based on the assumption that the image is increasing monotonically towards the centre in the unsaturated part of the image. The algorithm also calculates approximate stellar magnitudes and efficiently rejects most of the cosmics which would otherwise lead to a large number of false detections. The quality of the algorithm was evaluated with the help of a large set of simulated data for the DIVA satellite mission; different assumptions were made for the noise level, and the presence of cosmics or for a variable sky background.We could show that our algorithm fulfills the requirements for DIVA; only in the case of simulated images which included the bright galaxy M31 some fainter stars could not be detected in the galaxy's vicinity. Since stellar images contain large areas without any stars, we propose an additional block-skipping algorithm which can be coded on special-purpose hardware. This revised version was published online in July 2006 with corrections to the Cover Date.     

Multichannel Blind Deconvolution As A Tool For Solar Image Restoration

A principle of restoration methods based on multichannel blind deconvolution (MBD) is introduced. The methods assume that for every un-degraded unobservable image several degraded observed images are available. It is better conditioned than classical single channel approach. The first algorithm represents a generalization of iterative deconvolution scheme introduced for single images. The second MBD algorithm is based on so-called subspace technique. The subspace method is not iterative and this possibly implies an implementation that can be computationally more efficient. Both methods are presented in applications to artificial image data (computer-generated multichannel degraded data) with known ideal image to get a comparison with restored one. Performance in a real situation on solar photosphere images is shown.     

The Cannon:一种基于光谱数据的恒星参数测量方法

新一代大规模光谱巡天项目产生了近千万条低分辨率恒星光谱,基于这些光谱数据,介绍一种名为The Cannon的机器学习方法。该方法完全基于已知恒星大气参数(有效温度、表面重力加速度和金属丰度等)的光谱数据,通过数据驱动来构建特征向量,建立光谱流量特征和恒星参数的函数对应关系,进而应用到观测光谱数据中,实现对恒星光谱的大气参数求解。The Cannon的主要优势为不直接基于任何恒星物理模型,适用性更广;由于使用了全谱信息,即便对于低信噪比光谱也能得到较高可信度的参数结果,该算法在大规模恒星光谱的数据处理和参数求解方面具有明显的优势。此外,还利用The Cannon得到LAMOST光谱数据中K巨星和M巨星的恒星参数。