基于多圆迭代和H∞ 滤波的捷联/天文定位算法研究

针对现有天文定位算法的缺点,提出一种多圆交汇迭代天文定位算法.该算法能够充分利用可观测恒星,同时能计算出定位误差协方差阵.针对提出的算法定位误差非线性的特点,提出了基于H∞滤波器的捷联/天文自适应组合导航算法,根据定位误差协方差阵实现量测自适应.仿真结果表明,天文定位算法模型准确,组合导航算法定位精度小于20m,定位误差稳定.     

矢量天线阵元方向不一致的误差校正

基于特征值分解的子空间类算法对误差非常敏感,有必要对方向误差进行有效的校正.分析了方向误差形成的原因,建立了误差阵元的数据模型,研究同一入射信号玻印廷(Poynting)矢量与阵元排列方向的关系,使用3个参量未知的校正源,通过比较校正源玻印廷矢量在参考阵元与误差阵元处的响应,获得阵列采样数据方向误差校正矩阵.比较玻印延矢量的误差校正方法不涉及参量搜索,计算量小且易于在工程上实现,计算机仿真验证了算法的正确性和有效性.     

基于实值编码GA的卫星巡天扫描方法

巡天扫描是天文卫星进行天体观测的基础,但是使用传统方法很难实现全天球覆盖,并且会遇到能源输出瓶颈。因此,本文针对天文卫星巡天扫描所遇到的问题,提出了使用基于实值编码的遗产算法(Real-codedGA)对卫星飞行任务进行智能规划的方法。该算法智能规划飞行任务,与仅依靠被动的分段式扫描方法相比,很好解决了能源输出的问题,还大大缩短了巡天扫描的时间。从仿真结果来看,该模型是十分有效的。     

基于特征融合的步态识别算法

步态运动中包含人体形状信息和运动信息,目前步态识别算法多数基于单一信息,不能取得满意的识别结果。利用特征融合的思想,提出一种融合人体轮廓特征和下肢角度特征的步态识别算法。采用傅立叶描述子描述人体轮廓特征;区别于基于模型的运动特征提取方法,依据人体解剖学的知识获取下肢角度,计算代价较小;采用加权融合规则实现两类特征的融合。仿真结果表明,本算法的性能较基于单个特征的算法有明显的提高。     

基于Cauchy模型的行人轮廓提取及目标检测

为了提高行人目标轮廓参量的提取精准度数值,实现对待监测目标的实时稳定跟踪,提出基于Cauchy模型的行人轮廓提取及目标检测算法;基于Cauchy分布原理,估计行人轮廓目标的最大似然值,再结合计算第二类统计量方法,完成基于Cauchy模型的行人目标统计建模;在此基础上,建立卷积神经网络,利用卷积化与反卷积参量,提取Gabor行人轮廓特征;在目标图像分割理论的作用下,识别既定区域内的所有行人目标,持续标记各类已存在的行人目标,实时检测行人轮廓目标,实现基于Cauchy模型行人轮廓提取及目标检测;实验结果表明,与Kinect型检测算法相比,应用Cauchy型算法后,行人目标轮廓的检测精度值提高至93%,而PTR实测指标降低至3.97,可有效实现待监测行人轮廓目标的实时稳定跟踪。     

惯性/天文深组合非线性定位算法

针对传统惯性/天文定位算法进行天文导航定位时需要通过迭代计算获得载体的经纬度信息,会不可避免地引入定位计算误差问题,推导了天文高度角与平台误差角和水平位置误差的数学模型,进而提出了以天文高度角为量测信息的基于无迹卡尔曼滤波的惯性/天文深组合导航算法.仿真结果表明,所提出的算法利用单颗星即可进行定位,3颗星时组合导航定位精度达到100 m,有效提高了惯性/天文组合定位精度.     

科学数据网格中面向天文应用的问题求解环境设计与实现*

通过对虚拟天文台中科学课题的需求分析,提出一种基于数据网格针对天文应用的问题求解环境的框架;以"2MASS巡天星表搜寻OB星协候选体研究银河系的旋臂结构"科学课题为例,在科学数据网格上设计和实现了面向天文应用的问题求解环境,并对实现中的关键技术进行研究.     

有色噪声下的平方根UKF在天文自主导航中的应用

针对由星敏感器和光学导航相机组成的卫星天文自主导航系统, 传统的平方根UKF不能很好地解决测量噪声为有色噪声情况下的非线性滤波问题, 导致导航系统的精度下降. 为此, 提出了一种有色噪声情况下的平方根UKF方法. 同时, 为了避免在数值计算的过程中, 由于舍入误差而破坏误差协方差矩阵的正定性和对称性, 在整个递推计算过程中, 借鉴平方根Kalman滤波理论, 采用协方差矩阵平方根进行递推计算, 改善滤波算法的稳定性, 协方差矩阵的平方根更新用cholesky分解和qr分解来计算. 将该方法应用于卫星天文自主导航系统中, 实验仿真结果表明, 相对于传统的平方根UKF算法, 所设计的平方根UKF算法能够很好地解决测量噪声为有色噪声情况下估计精度低问题.     

高分六号卫星遥感估算大气细颗粒物方法研究北大核心CSCD

针对目前利用高分六号卫星开展地区高空间分辨率大气细颗粒物监测研究较少的问题,提出了基于高分六号卫星宽幅相机数据的气溶胶光学厚度及大气细颗粒物遥感反演的技术方法,并在京津冀及周边地区开展了应用实验和对比分析。首先,基于改进暗像元法反演了高分六号卫星数据的气溶胶光学厚度;然后,结合地面大气细颗粒物监测数据与多种气象辅助数据,基于随机森林算法,构建了多参量综合的大气细颗粒物估算模型,对京津冀地区的大气细颗粒物浓度进行了估算。研究表明:高分六号气溶胶光学厚度反演结果与地基站点监测结果的相关系数为0.94,反演精度较高;大气细颗粒物估算结果与地基站点监测结果的决定系数达到0.79以上,较好地反映了京津冀地区的大气细颗粒物空间分布。     

HJ-1A/B星地表反照率生成算法与检验

提出了适合环境与灾害监测预报小卫星-A、B星(简称HJ-1A/B星)CCD相机的大气订正算法,并基于不同地表特性和大气条件下的辐射传输模拟数据,建立HJ-1A/B星的窄波段向宽波段反照率转换的模型.利用多级灰阶靶标实测数据、敦煌检验场实测数据验证了大气订正算法以及转换模型的可靠性和精度,并将HJ-1A/B星影像数据计算的反照率产品与同时相的MODIS反照率产品进行对比分析.结果表明:文章提出的HJ-1A/B星CCD相机大气订正算法可有效校正大气影响;窄波段向宽波段反照率转换模型反演的反照率精度可靠;基于研究成果生成的HJ-1A/B星地表反照率与MODIS反照率产品一致性较好,满足后续遥感数据定量化模型研究的精度需要.     

具有身份标识的多目标跟踪

针对多目标跟踪,运动目标身份标号在目标发生遮挡、交错时容易混淆的问题,提出一种基于颜色特征信息的多目标跟踪算法。即在目标跟踪过程中,充分利用背景减除法所获取的前景团块区域,对其进行有效分类,如噪声区域、单目标区域和多目标区域,根据分类情况采用不同的处理机制。算法利用修正时间印机制处理噪声区域,利用Kalman预测处理快速运动,利用均值移动算法处理目标标识混淆问题。通过多组实验可以看出,新算法处理速度达到30帧/s、实时性能好,具有很强地抑制背景干扰、目标长时间跟踪的特性。     

Intelligent feature extraction and classification of anuran vocalizations

An intelligent identification system for mixed anuran vocalizations is developed in this work to provide the public to easily consult online. The raw mixed anuran vocalization samples are first filtered by noise removal, high frequency compensation, and discrete wavelet transform techniques in order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Six features, including spectral centroid, signal bandwidth, spectral roll-off, threshold-crossing rate, spectral flatness, and average energy, are extracted and served as the input parameters of the classifier. Meanwhile, a decision tree is constructed based on several parameters obtained during sample collection in order to narrow the scope of identification targets. Then fast learning neural-networks are employed to classify the anuran species based on feature set chosen by wrapper feature selection method. A series of experiments were conducted to measure the outcome performance of the proposed work. Experimental results exhibit that the recognition rate of the proposed identification system can achieve up to 93.4%. The effectiveness of the proposed identification system for anuran vocalizations is thus verified.     

Adaptive Impulsive Noise Removal in TV Picture Transmission

This paper presents the capability and real-time processing features of an adaptive filter for the removal of impulse noise in TV picture transmission. The basic method, which has been extensively tested on data corrupted by randomly distributed impulses, is first enhanced to deal with real TV pictures suffering from impulse noise. In particular, the characteristics of the real impulses are incorporated in the noise identification stage. Then the suitability of the method in terms of real-time processing for images corrupted by randomly distributed impulse noise and those corrupted by real impulse strokes is analytically and numerically investigated. Impulse corrupted TV picture sequences are used to demonstrate that the proposed method potentially provides a real-time solution to quality TV picture transmission.     

A method of sensor fault detection and identification

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.     

基于视觉的大鼠肢体运动自动分析系统

设计并实现了实验大鼠肢体运动自动分析系统,系统由通用视频设备和计算机硬件组成,采用基于颜色标记的运动捕捉技术,提出了一种RGB空间映射到HSV空间的快速转换方法,以及快速颜色分类算法结合轮廓搜索算法得到标记点坐标,提取肢体运动学特征参数,并应用于步态行为分类和识别.实际测试平均每帧处理时间小于10ns,识别正确率97％...     

Neuromolecularware and its application to pattern recognition

Unlike computer systems, organisms have high adaptability in dealing with environmental changes or noise. The ability to evolve, self-organizing dynamics, and a closed structure–function relationship are the three principle features embedded in biological structures that provide great malleability to environmental change. Computer systems have fast processing speed for performing heavy computational tasks. One of the objectives in this research is to capture these three biological features and implement them onto a digital circuit. The proposed hardware (called neuromolecular hardware), is the integration of inter- and intraneuronal information processing applied to the pattern recognition problem domain. This approach was tested on the Quartus II system, a simulation tool for digital circuits. The experimental result showed good self-organizing capability in selecting significant bits for differentiating patterns and insignificant bits for tolerating noise. The proposed digital circuit also exhibited a closed structure–function relationship. This implied that this hardware embraced an adaptive fitness landscape that facilitated processing spatiotemporal information.     

The RightPages image-based electronic library for alerting and browsing

The RightPages electronic library prototype system, which gives users full online library services, is described. The prototype takes advantage of fast hardware, multimedia workstations, and broadband networks to process scientific and technical journals for users and to offer a service that: alerts them to the arrival of new journal articles matching their interest profiles; lets them immediately examine images of pages in the alerted articles and browse through other articles in the database; and enables them to order paper copies of any articles in the database. The system runs on a local area network that connects one or more scanning stations, a centralized document database server and multiple user stations running X Windows servers. The RightPages interface runs as an X Windows application on Sun workstations or X terminals. The system's image and document processing, including noise reduction, document layout analysis, text processing, and display processing are discussed.<>     

Fuzzy service selection in a distributed object-orientedenvironment

In network-centered execution environments, client objects can transparently invoke services offered by remote server objects, according to their published interface. The object selection problem requires the evaluation of the fitness of a pool of candidate server objects on the basis of the available information about their functional and nonfunctional features. Network-centered systems usually store such information in a trader agent that can be browsed or queried by client objects. In this paper, a fuzzy data model is proposed as the basis of the design of such a trader system, taking into account synergy between objects' features. Our trader is based on a fuzzy query algebra allowing for deriving operator definitions (therefore, query execution mechanisms) at run time, on the basis of user-selected semantics     

Ship identification in sequential ISAR imagery

We have developed an online system that automatically identifies ships observed in a rapidly updating sequence of range-Doppler images produced by inverse synthetic aperture radar (ISAR). In the system, in order to cope with the invariable noise due to the physics of imaging, we propose to employ a multiframe image processing algorithm that stably extracts profiling as a basic feature reflecting all characteristics of a target. For ship identification, representing the extracted profiles as high-dimensional vectors, we adapt the vector analysis using the recently proposed constrained mutual subspace method (CMSM). The system currently works on an ordinary PC at 5 frames/s and achieves feasible performance of identification. The system is verified using simulated data.Received: 22 September 2002, Accepted: 22 March 2004, Published online: 27 May 2004 Correspondence to: Atsuto Maki. Currently with the Graduate School of Informatics, Kyoto University     

计算机虚拟仪器技术在巴克豪森检测仪上的应用

近年采,虚拟仪器技术在各行业应用中快速发展,特别在复杂信号处理及数据存储量大的应用领域拥有优势.针对原有巴克豪森检测仪在数据处理和数据存储方面的不足,研究并开发了基于计算机虚拟仪器技术的巴克豪森检测仪.介绍了该系统的原理、结构、特性,还指出了其在铁路、汽车制造等领域的应用前景.