自动制图综合人工神经元网络方法的研究

现代神经生理学研究表明 ,人脑的大量神经元构成有所分工而又紧密联系的神经元网络 ,它的结构和功能可以采用物理可实现的系统人工神经元网络来模拟。制图综合是人脑神经元网络获取、处理、输出地理信息的复杂视觉思维过程 ,可以用人工神经元网络来模拟。文中探讨了制图综合的人工神经元网络的设计 ,并对用于实现自动制图综合的结果进行分析 ,指出其应用前景。     

GIS数据模型研究与实践

着重分析了 GIS数据建模、地理数据的 3层结构及面向对象的数据模型 ,并在此基础上提出了一种基于关系数据库的面向对象 GIS数据模型     

利用遥感和GIS研究塔里木河下游阿拉干地区土地沙漠化

新疆塔里木河流域受人类活动的影响,特别是由于水资源利用的不合理,不同区域出现了一系列生态环境问题,下游地区普遍存在的沙漠化现象表现得尤为突出。通过应用多时相（１９５９年、１９８３年、１９９２年）、多波段、多平台的遥感信息,在野外调研的基础上编制阿拉干地区不同年代沙漠化类型图,并在ＡＲＣ／ＩＮＦＯ软件支持下,对图件进行编辑处理,制作沙漠化动态图;通过ＧＩＳ数据库提供的资源环境定量数据,应用系统论、信息论及控制论的观点分析阿拉干地区沙漠化的演化过程,并借助于ＧＭ（１,１）模型,预测阿拉干地区土地沙漠化的发展趋势。     

利用SIR-C极化数据提取地面参数

本文根据简化的积分公式模型（ＩＥＭ）,分析了面散射过程中后向散射系数与地面参数之间的关系。利用航天飞机成像雷达（ＳＩＲ－Ｃ）获取极化的雷达图像,提取新疆北部地区冲扇的散射系数以及介电常数（湿度）与粗糙度。由图像获得的地面参数数据,可以用于分布冲积扇成因、时代以及其次的关系。     

基于地理信息系统的数字环境模型研究

本文提出了以遥感与地理信息系统为技术手段,用有序数值阵列的方法建立数字环境模型的新思路,具体阐述了建立数字环境模型的意义、方法和应用范围。并在建立数字环境模型的基础上,对西藏“一江两河”地区的环境综合状况进行了定量评价。     

论地球数据科学与共享

地球科学大数据的到来,迫切需要通过地球数据科学系统对地学数据机理、采集处理、存储管理,特别是集成共享与挖掘分析等进行研究,以便通过大数据资源,发现和揭示数据背后隐含的地学规律和知识,促进地学科技创新。本文首先分析了地球数据科学的内涵特征,提出了地球数据科学的基础理论方法和技术体系。然后基于国家地球系统科学数据共享实践,重点研究了大数据时代下,面向地球数据科学的地学数据共享新发展。     

乌鲁木齐市建设用地全程管理系统分析与设计

建设用地全程监管工作是促进节约集约用地的重要手段,而建设建设用地全程管理系统则是有效落实建设用地全程监管工作的的重要工具。结合乌鲁木齐市国土资源局信息化现状及建设用地业务需求,分析了建设用地业务及内容,提出了系统的建设目标、建设思路、总体框架、功能结构。     

Unbalanced AltBOC: a Compass B1 candidate with generalized MPOCET technique

With the proposed new GNSS signals, enhanced navigation performance is expected in both civil and military applications. However, these new signals introduce the difficulty of combining multiple signal components into a constant-envelope signal. For the Compass B1 band, the problem is to multiplex a QPSK(2) signal and a new multiplexed binary offset carrier (MBOC) signal with a center frequency difference of 14.322 MHz. One approach for multiplexing spreading codes is the phase-optimized constant-envelope transmission (POCET) method proposed for the GPS L1 band. However, only binary spreading codes are considered in POCET. We first generalize the POCET method as a multilevel POCET (MPOCET) algorithm for multilevel coded signals. A new implementation of the alternative binary offset carrier (AltBOC) generator is derived from MPOCET. Secondly, the multiplexing problem for Compass is modeled by MPOCET. Multilevel subcarriers of AltBOC are adopted in the model. As a result, an 8-PSK unbalanced AltBOC (UAltBOC) modulation, which has a QPSK(2) signal at the lower sideband and a TMBOC signal at the upper sideband, is obtained. Simulations for signal model validation and power spectrum analysis are conducted. Numerical results indicate that UAltBOC successfully combines the QPSK and TMBOC signals with only 0.16-dB additional combining loss compared to AltBOC. The proposed MPOCET technique is demonstrated as a unified multiplexing method for navigation signals.     

Outlier separability analysis with a multiple alternative hypotheses test

Outlier separability analysis is a fundamental component of modern geodetic measurement analysis, positioning, navigation, and many other applications. The current theory of outlier separability is based on using two alternative hypotheses—an assumption that may not necessarily be valid. In this paper, the current theory of outlier separability is statistically analysed and then extended to the general case, where there are multiple alternative hypotheses. Taking into consideration the complexity of the critical region and the probability density function of the outlier test, the bounds of the associated statistical decision probabilities are then developed. With this theory, the probabilities of committing type I, II, and III errors can be controlled so that the probability of successful identification of an outlier can be guaranteed when performing data snooping. The theoretical findings are then demonstrated using a simulated GPS point positioning example. Detailed analysis shows that the larger the correlation coefficient, between the outlier statistics, the smaller the probability of committing a type II error and the greater the probability of committing a type III error. When the correlation coefficient is greater than 0.8, there is a far greater chance of committing a type III error than committing a type II error. In addition, to guarantee successful identification of an outlier with a set probability, the minimal detectable size of the outlier (often called the Minimal Detectable Bias or MDB) should dramatically increase with the correlation coefficient.