基于多源信息融合的浅海超低频声源目标探测关键技术及实现

以多传感器信息融合理论为指导,结合现代信息处理技术与数据驱动建模及科学计算技术,研究浅海超低频声源目标激发共存地震波的复合声场中超低频声波传播特性和数据驱动建模的水下目标深度识别等关键技术,并论证了技术实现方案及路径。结果表明:以复合矢量水听器、地震波监测仪等多传感器信息融合理论为指导研究前海超低频声源目标可以克服传统声场建模存在的问题,有助于浅海超低频声源目标探测及改善海洋水下声学监测手段。在提高声呐探测设备的测量准确度、精度方面具有重要的理论意义,对周边海域为浅海的我国海防具有实战价值。     

结合深度学习和图割法的遥感影像建筑物检测

提出一种基于卷积神经网络和图割法的自动提取高分影像建筑物的方法。首先,通过卷积神经网络定位与检测建筑物的位置,逐一提取单个建筑物轮廓,利用检测结果分别建立建筑物和非建筑物的高斯混合模型（GMM）,然后结合最大流最小割的图像分割方式实现全局优化,完成建筑物初步提取,最后用形态学进行优化。通过试验证明了该方法的可行性。     

多源空间数据融合的城市精细化管理

城市管理体量不断增大且管理对象关联繁多,已经成为影响城市可持续发展的重要问题。在全国智慧城市建设的大背景下,引用新技术提升城市精细化管理水平可以使城市的各管理单元协同、精确、及时地交换信息,形成智慧城市管理的配套方案,让有限的管理资源发挥出最大的使用效能,实现城市管理的最优化目标,促进经济的可持续发展。本文提出了地理信息系统、GPS、遥感、RFID等空间数据融合的技术方案,强调在空间信息和移动信息管理的基础上通过工作流和过程化管理,提高城市精细化管理的综合水平,为实现民众参与监督城市管理和智慧城市建设提供参考。     

对基础测绘"十四五"规划的若干思考

着眼于明确机构改革后基础测绘在自然资源管理业务格局中的职责定位、工作重点,以及如何按照新《测绘法》更好地履行"为经济建设、国防建设、社会发展和生态保护服务"的法定要求,分析了"十四五"基础测绘发展的基本形势,据此提出"十四五"规划工作的基本思路和主要遵循,明确了工作重点和相关措施,相关观点已经在"十四五"基础测绘规划编制工作中得到体现。     

PDR算法对地磁室内定位精度的提升研究

利用建筑物中金属结构引起的地磁场扰动可以对室内的行人目标进行定位,而且基于地磁场的定位无需布设任何额外设施,因此可以以低成本实现定位。但仅靠单一的地磁技术无法满足室内定位的精度要求。为了解决磁场数据中单点定位的模糊性问题,本文提出了一种利用粒子滤波算法将PDR与地磁相融合的室内定位方法,并开发了地磁室内导航系统,以智能手机为硬件平台构建磁力计传感器模型,建立匹配轨迹的均方误差准则并实现PDR累积误差实时校正的迭代计算。在68 m×1.8 m的试验区域内,产生的平均定位误差为1.13 m,最大定位误差为2.17 m。本文算法的定位精度比单独PDR算法提升了42%;与单一地磁指纹匹配算法相比,定位精度提高了57%。试验证明,本文提出的融合算法对提高室内定位精度具有显著的作用。     

兼顾志愿者地理信息的国家应急测绘公众服务平台设计

随着互联网技术的进步和新一代网络信息传播模式的深入发展,志愿者地理信息的研究和应用日益广泛,但由于其规范性和均一性方面的缺陷,在应急公共服务领域,志愿者信息的有效利用依旧是亟需突破的难题。本文将重点介绍国家应急测绘公众服务平台设计思路和总体架构,并结合志愿者地理信息特点,探讨该类信息在国家应急测绘公众服务平台建设中的处理流程和应用模式,力求在应急信息资源共享和服务平台共建方面进行新的尝试,为我国突发事件应急体系建设提供有益实践。     

语义因子支持的多源POI分类信息一致化处理

为了解决网络POI分类异构导致多源POI数据匹配与整合困难的问题,该文利用分类特征词提取、特征词去重与优化等关键技术,构建POI分类的语义概念格,然后通过概念格之间的语义关联关系,实现多源异构POI分类体系的映射与转换。最后通过实验验证了该文方法的有效性,并将其成功应用于省级地理信息公共服务平台(政务版)数据更新。     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

A novel spatial index using spatial analyses and hierarchical fuzzy expert system for obtaining green TOD: a case study in Tehran city

Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning.