Assessment of code bias variations of BDS triple-frequency signals and their impacts on ambiguity resolution for long baselines

GPS Solutions - Carrier phase ambiguity resolution over long baselines is challenging in BDS data processing. This is partially due to the variations of the hardware biases in BDS code signals and...     

Design of an adaptive GPS vector tracking loop with the detection and isolation of contaminated channels

GPS Solutions - In vector tracking loop (VTL), the relativity among received signals is exploited to deeply integrate the entire information within signal processing channels. However, the tracking...     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

智能手机音频信号与MEMS传感器的紧耦合室内定位方法

基于智能手机内置的传感器,开展了音频信号和MEMS传感器的紧组合室内定位研究.首先,将时频分析技术用于音频信号TOA估计,提出了一种基于短时傅里叶变换和优化互相关方法的两步估计方法.之后,研究了一种基于音频T DOA和行人航迹推算的紧组合定位算法,静态测试的平均定位精度为0.238 m,相比最小二乘方法提高了38.66...     

地面激光扫描技术在建筑变形测量中的应用探讨

已有不少文献针对激光扫描在建筑变形测量中的应用进行了研究,但是国家和行业尚无三维激光扫描技术规程出台。本文通过应用调研、扫描仪的标称精度统计分析,以及具有针对性的试验,明确了激光扫描测量建筑变形的作业方法,确立了选用仪器精度、扫描测量的测回数、标靶入射角度等技术指标,为激光扫描技术在建筑变形测量中的工程应用提供了参考依据。     

基于三维激光扫描技术的盾构隧道变形分析

为保证地铁隧道建设过程中的安全,对其进行变形监测是必不可少的工作.传统测量方法采用特征点位进行隧道断面监测,存在数据量较少、点位不够全面等缺点,且工作量较大,工作效率低.针对这一问题,本文将三维激光扫描技术应用于盾构隧道监测,利用获取的三维激光点云对盾构隧道进行监测.为验证其可靠性,将其与全站仪数据进行联合分析.实验表...     

月度亚米级卫星影像在"两违"监测中的应用

严厉打击违法用地和违法建设行为,能保护国土资源不受侵害、提升城市形象、维护社会公平和国家法律法规.利用卫星影像监测"两违"行为,具有覆盖范围广、监测频率高、成本低、受限制条件少等优势,随着卫星影像空间、时间分辨率的逐渐提高,使月度亚米级卫星影像在"两违"监测中的应用成为可能.研究利用月度亚米级卫星影像开展"两违"监测应用的方法,以北京市为例介绍了具体应用情况,并总结了技术关键点.利用高空间分辨率、高频次卫星影像可以有效提高"两违"监测的准确性和时效性,内业变化检测和外业核查相结合的方式可为"两违"执法提供更为客观准确的数据基础和决策依据.     

归类识别地名匹配算法

针对现有中文地名匹配算法忽略中文表达的多样性和规则性使得匹配准确性不足的问题,文章提出了一种归类识别地名匹配算法。该算法首先基于地名通名词典和地名结构规则知识库对地名类型判定,然后进行地名核心词字符串相似度匹配,最终得到符合查找期望的匹配结果。论文以居民小区地名为具体研究对象,利用该算法对德州市600条已售小区数据进行匹配验证,试验结果表明该算法在匹配效率和准确性两方面有较大提升。     

三维激光扫描技术点云数据采集与配准研究

点云数据采集与配准的精度决定了建筑物三维建模的精度。围绕建筑物点云数据采集与配准的精度问题,分别采用基于标靶的模式、基于形状匹配的模式和基于测站后视的模式进行建筑物表面点云数据的采集与配准处理,并从配准精度、特征点、特征边3个方面进行了对比分析。结果表明,基于形状匹配的模式的精度较低,其他两种模式克服了"同名点"的问题,在配准精度上有明显优势;而在数据采集与配准整体效率方面,基于测站后视的模式优于其他两种模式。