共查询到20条相似文献，搜索用时 171 毫秒
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顾及声线入射角的水下定位随机模型 总被引：1，自引：1，他引：0
水下定位中广泛采用的等权随机模型,实现简单但与实际不符,影响水下定位精度。针对该问题,结合水下定位实际,考虑水声测量误差和声线弯曲误差影响,在分析随机模型不完善条件下参数估计性质的基础上,构建了4种基于声线入射角的水下定位随机模型。通过模拟算例和实测数据进行了验证。结果表明：所构建的4种入射角随机模型比等权模型在定位精度上具有一定优势,其中分段余弦模型定位结果最好;入射角阈值取值在40°~50°范围内时,定位精度最高;100 m以内水深环境下,基于声线入射角的随机模型改善了传统等权模型的定位结果。 相似文献
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GPS单站定位的作业方式、外业组织观测和数据处理等比较简单,同时,由于采用观测值不同、观测时间长短不同、定位模式和数据处理方式不同,其精度可以从毫米级到米级不等,实际应用中以伪距单点定位和精密单点定位为主。GPS单站定位不能通过求差方式来消除或消弱各项误差,为提高其定位精度,必须研究各项误差的改正模型,以便对其进行精细改正。本文用2010年10月31日CHAN站24小时的观测数据,确定各项误差在一天中的影响大小和变化规律,研究结果对精密单点定位的误差研究和GPS定位误差的教学工作等都有参考意义。 相似文献
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Digital elevation model (DEM) source data are subject to both horizontal and vertical errors owing to improper instrument operation, physical limitations of sensors, and bad weather conditions. These factors may bring a negative effect on some DEM-based applications requiring low levels of positional errors. Although classical smoothing interpolation methods have the ability to handle vertical errors, they are prone to omit horizontal errors. Based on the statistical concept of the total least squares method, a total error-based multiquadric (MQ-T) method is proposed in this paper to reduce the effects of both horizontal and vertical errors in the context of DEM construction. In nature, the classical multiquadric (MQ) method is a vertical error regression procedure, whereas MQ-T is an orthogonal error regression model. Two examples, including a numerical test and a real-world example, are employed in a comparative performance analysis of MQ-T for surface modeling of DEMs. The numerical test indicates that MQ-T performs better than the classical MQ in terms of root mean square error. The real-world example of DEM construction with sample points derived from a total station instrument demonstrates that regardless of the sample interval and DEM resolution, MQ-T is more accurate than classical interpolation methods including inverse distance weighting, ordinary kriging, and Australian National University DEM. Therefore, MQ-T can be considered as an alternative interpolator for surface modeling with sample points subject to both horizontal and vertical errors. 相似文献
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In this paper, we use control methodologies based on lines to detect the type of positional errors which affect a spatial database (SDB) and more concretely the presence of systematic errors. The methodology involves determination of the displacement vectors between the lines and a graphical and statistical study of the components obtained. The graphical analysis enables the detection of spatial patterns of displacement; the presence or absence of systematic errors is then confirmed by statistical analysis. We have applied this method to detect systematic errors on a set of lines with introduced displacements, rotations and scale changes. The results show the viability of the method. All the bias effects introduced were detected, both qualitatively and quantitatively. The detection method has the potential to minimize the effects of such displacements in the SDBs. 相似文献
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分别利用直线、圆曲线与多项式曲线的拟合空间曲线实体,估计出拟合曲线与真实曲线之间的模型误差,建立包含模型误差与法线方向位置误差的曲线综合误差带模型。并通过算例证明了含有模型误差的综合误差带模型能更好地反应圆曲线的位置不确定性。 相似文献
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Positional Accuracy of Spatial Data: Non-Normal Distributions and a Critique of the National Standard for Spatial Data Accuracy 总被引：5，自引：0，他引：5
Paul A Zandbergen 《Transactions in GIS》2008,12(1):103-130
Spatial data quality is a paramount concern in all GIS applications. Existing spatial data accuracy standards, including the National Standard for Spatial Data Accuracy (NSSDA) used in the United States, commonly assume the positional error of spatial data is normally distributed. This research has characterized the distribution of the positional error in four types of spatial data: GPS locations, street geocoding, TIGER roads, and LIDAR elevation data. The positional error in GPS locations can be approximated with a Rayleigh distribution, the positional error in street geocoding and TIGER roads can be approximated with a log‐normal distribution, and the positional error in LIDAR elevation data can be approximated with a normal distribution of the original vertical error values after removal of a small number of outliers. For all four data types considered, however, these solutions are only approximations, and some evidence of non‐stationary behavior resulting in lack of normality was observed in all four datasets. Monte‐Carlo simulation of the robustness of accuracy statistics revealed that the conventional 100% Root Mean Square Error (RMSE) statistic is not reliable for non‐normal distributions. Some degree of data trimming is recommended through the use of 90% and 95% RMSE statistics. Percentiles, however, are not very robust as single positional accuracy statistics. The non‐normal distribution of positional errors in spatial data has implications for spatial data accuracy standards and error propagation modeling. Specific recommendations are formulated for revisions of the NSSDA. 相似文献
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A heuristic‐based approach to mitigating positional errors in patrol data for species distribution modeling 
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下载免费PDF全文 Species distribution modeling (SDM) at fine spatial resolutions requires species occurrence data of high positional accuracy to achieve good model performance. However, wildlife occurrences recorded by patrols in ranger‐based monitoring programs suffer from positional errors, because recorded locations represent the positions of the ranger and differ from the actual occurrence locations of wildlife (hereinafter referred to as positional errors in patrol data). This study presented an evaluation of the impact of such positional errors in patrol data on SDM and developed a heuristic‐based approach to mitigating the positional errors. The approach derives probable wildlife occurrence locations from ranger positions, utilizing heuristics based on species preferred habitat and the observer's field of view. The evaluations were conducted through a case study of SDM using patrol records of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) in Yunnan, China. The performance of the approach was also compared against alternative sampling methods. The results showed that the positional errors in R. bieti patrol data had an adverse effect on SDM performance, and that the proposed approach can effectively mitigate the impact of the positional errors to greatly improve SDM performance. 相似文献