基于参考轨迹的多无线电组合导航系统三维空间覆盖分析方法

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2020-09-15 DOI:10.1080/19479832.2020.1813814

J. Son, S. Oh, D. Hwang

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引用次数: 0

摘要

当导航战导致GPS信号不可用时，GPS可以与其他无线电导航系统集成。在部署导航信号源之前，可以进行覆盖分析，以检查在二维空间中是否满足所需的导航性能。通常，覆盖分析是针对车辆运行的区域进行的。当飞行器运行时，将该方法直接扩展到二维空间，计算量会过大。为了减轻三维空间的计算负担，提出了一种基于参考轨迹的多无线电组合导航覆盖分析方法。对2种轨迹和6种导航信号源布置进行了性能评价。结果表明，该方法可用于三维空间的覆盖分析。

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Reference trajectory-based coverage analysis method in three-dimensional space for multi-radio integrated navigation systems

ABSTRACT GPS can be integrated with other radio navigation systems when GPS signals are not available due to navigation warfare. Before deploying navigation signal sources, the coverage analysis can be performed in order to check a required navigation performance is satisfied in 2-dimensional space. Usually, the coverage analysis is performed for the area in which a vehicle is operated. When an air vehicle is operated and the method in 2-dimensional is directly extended, the computational load can be excessively heavy. In this paper, a reference trajectory-based coverage analysis method for multi-radio integrated navigation systems is proposed using CRLB in order to alleviate computational burden in 3-dimensional space. The performance of the proposed method is evaluated for 2 trajectories and 6 arrangements of navigation signal sources. The results show that the proposed method can be used in the coverage analysis in 3-dimensional space.

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).