Network-based triple-frequency carrier phase ambiguity resolution between reference stations using BDS data for long baselines

Network-based ambiguity resolution (AR) between reference stations is the prerequisite to realize a precise real-time kinematic positioning service. With the help of BDS triple-frequency signals, we can efficiently deal with the ionospheric delay and tropospheric delay, and achieve rapid and reliable AR. To overcome the inaccurate ionospheric delay estimated by the geometry-free three carrier ambiguity resolution (GF TCAR) technique, which leads to failure in the original ambiguity resolution, we propose an ionospheric-free (IF) TCAR method to resolve the ambiguity between the reference stations over long baselines. Taking full advantage of the known positions of the reference stations, the easily resolved extra-wide-lane (EWL) ambiguity, and the IF phase combinations, we can reliably fix the wide-lane (WL) ambiguity. A Kalman filter is applied to estimate precise IF ambiguities and the original ambiguity is resolved with the fixed WL ambiguity. A numerical analysis with triple-frequency BDS data from three long baselines of a CORS network is provided to compare the AR performance of GF TCAR with that of IF TCAR. The results show that both methods can reliably resolve the WL ambiguity with a remarkable correctly-fixed rate of higher than 99%, and the reliably-fixed rates of the IF TCAR slightly increase from 92.19, 94.67 and 94.61–98.26, 99.54 and 97.51% for the three baselines. Herein “correctly-fixed” and “reliably-fixed” mean the difference between the float ambiguity and the true one are less than ± 0.5 and ± 0.25 cycles, respectively. On the other hand, the AR performance of the original signals with the IF TCAR method is much better than that with the GF TCAR method attaining a 100% correctly-fixed rate, while the GF TCAR method can hardly fix the original ambiguity with the largest bias being as much as 4 cycles because of the amplified systematic bias.     

A new multipath mitigation method based on adaptive thresholding wavelet denoising and double reference shift strategy

Multipath disturbance is one of the major error sources in high-accuracy positioning for global navigation satellite system (GNSS). Although various methods based on software and hardware strategies have been developed to mitigate this error, they are still limited by different kinds of factors and the effect is not ideal. After analyzing the existing methods, a new single-difference sidereal filtering method, based on adaptive thresholding wavelet denoising and double reference shift strategy (ATDR), is proposed to mitigate multipath effects for static short-baseline GNSS applications. The key idea of the proposed method is the use of both the adaptive thresholding wavelet denoising to extract an accurate multipath correction model from the reference Day and the double reference shift strategy to mitigate multipath for subsequent Day 2 more accurately and efficiently. By applying the introduced adaptive thresholding method, the average improvement rate of the RMS values of the single-difference residuals can reach about 15.82% compared with the constant thresholding method. Moreover, after applying the proposed ATDR method, the 3D positioning precision is improved by about 37.73% for the single epoch mode with 30 s data sampling rate and about 31.22% for the continuous mode with 1 s high sampling rate compared with the original results. Even compared with the constant thresholding single orbital reference (CTSR) method, the improvement percentage is about 33.94% in single epoch mode and about 25.40% in continuous mode for 3D positioning precision, respectively. In conclusion, the results of the two experiments indicate that the proposed ATDR method performs much better than the CTSR method in mitigating multipath for different sampling rates and different processing modes in the measurement domain for GNSS static short-baseline postprocessing applications.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.     

Examining human heat stress with remote sensing technology

Heat stress as an environmental hazard which can seriously affect productivity, health, or even survival of individuals has long been studied. Despite the endeavors that have been made to address the issue quantitatively with various heat stress indices, they are often measured at scatter sites. This research devotes to revealing human heat stress within continuous space with remote sensing technology. The study began with the retrieval of dry-bulb temperature from land surface temperature (LST) with empirical models. As wet-bulb temperature was calculated from dry-bulb temperature and relative humidity, discomfort index (DI) as an indicator of heat stress was revealed for the study area at 1 km spatial resolution for three summer days. Results indicated that DI can be derived within continuous space with remotely sensed data, and its spatial distribution can be dramatically affected by relative humidity. Further comparison between DI and LST indicated that LST as a widely utilized indicator of surface thermal condition fails to address human heat stress as environmental factors such as relative humidity are not taken into account.     

基于DEM的6种坡度提取算法的比较分析——以山东省青岛市为例

坡度是描述地形特征信息的一个重要指标,对于制订土地利用规划和防御地质灾害具有重要意义。本文基于山东省青岛市的ASTER GDEM数据,利用MATLAB进行矩阵运算,采用6种坡度提取算法分别获取坡度矩阵、最大值和中误差等统计指标,对比分析6种算法的提取精度。结果表明:简单差分算法出现极端和偏差情况的概率较高,可信度不高;三阶不带权差分和三阶反距离平方权差分算法比较稳定,偏差较小且精度较高,对沿海地区坡度提取具有借鉴意义。     

测绘应急保障测绘装备和资料的管理研究

通过建立测绘装备和测绘资料数据库,进行装备和资料的查询、分发操作,满足应急保障分队的测绘保障,提高了应急测绘保障能力。     

城市公共管理平台的地理信息可视化技术

城市公共管理对地理信息数据有着迫切的需求。本文从如何将海量地理信息与城市公共管理服务相结合入手,分析了目前城市公共管理平台地理信息可视化存在的问题,提出了用面向资源的地理信息服务共享模式,构建了城市公共管理云平台架构,实现了海量多元数据的纵横联通和有效集成。在此基础上通过浏览器端地理信息可视化架构,帮助用户提高视觉注意力,可以更好地为政府决策、社会公益服务等提供有效的地理信息服务支持。从我们建立的湖北省襄阳市城市公共管理平台来看,通过面向资源的一体化可视化技术,能够将城市地理信息数据与公共管理服务相结合,更有利于提高城市公共管理信息可视化的查询效率。     

二三维一体化的城市三维仿真应用系统设计与实现——以琶洲为例

随着城市化进程的深入发展,传统二维GIS应用系统的空间分析与表达已无法满足城市信息化管理的需求。三维GIS系统相对于二维GIS系统具有直观、准确的优点,是支撑智慧城市建设的核心技术。本文以广州市海珠区琶洲街为范围,设计了二三维一体化的城市三维仿真应用系统的体系结构,介绍了系统建设的技术路线,最后对应用系统的部分软件功能予以展示。本系统建设流程主要包括数据建设和应用系统开发两大部分,本文均予以详细阐述。由于需对海量的二三维数据进行融合存储并实时调度,而本系统底层利用的Skyline三维引擎支持实时的超大数据融合,因此,本系统具有良好的三维数据运载能力。     

北斗二代双频数据质量分析

对北斗二代卫星的双频观测数据进行了质量分析,包括伪距多路径误差和信噪比两个方面。大量实测数据测试表明:北斗GEO、IGSO和MEO三类卫星的多路径误差逐渐增大,且B1频点小于B2频点,GPS与北斗MEO卫星的伪距多路径误差相差不大;另外,北斗三类卫星均表现出载波B2的信噪比略大于B1,而GPS卫星则表现出L1载波信噪比要大于L2。     

用于海上溢油监测的航海雷达图像降噪技术研究

航海雷达溢油监测技术具有大范围目标探测、工作方式灵活、能够适应恶劣天气的优势。而海上溢油监测技术应用的航海雷达原始图像数据中,含有大量的同频干扰、斑点、假目标等噪声,处理上述噪声是航海雷达海上溢油监测技术研究的首要工作。本文基于先锁定噪声位置,再消除噪声的思想,提出了一种有效的航海雷达原始回波图像的降噪技术。以大连716溢油事故中采集得到的数据进行试验,并分析处理结果,这种方法不仅能够很好地消除干扰噪声,而且能够有效地保护航海雷达原始回波图像中的海浪信息,为溢油监测技术研究提供了良好的数据基础。