存在邻近干扰源的GNSS干扰影响空间范围分析
Spatial Boundary Analysis of GNSS Interference with Adjacent Interference Sources
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摘要: 当前全球卫星导航系统(Global Navigation Satellite System,GNSS)应用广泛,近年来各种新型无线电干扰不断出现,使GNSS应用系统的稳健性受到影响。GNSS在民航飞机运行中也正在发挥越来越重要的作用,针对民航飞机GNSS信号干扰的问题,国内外已开展了利用民航飞机数据进行干扰源定位的研究,现有相关研究一般假设分析空间中仅存在单个干扰源,而在实际中发现了一些干扰民航GNSS信号的干扰源附近存在其他GNSS干扰源的案例。干扰源影响空间范围的分析对于指导干扰源定位、有效排查干扰源具有重要意义。本文针对干扰GNSS信号的主干扰源周围存在邻近干扰源的情况下,邻近干扰源对主干扰源的干扰空间范围在不同参数下的影响程度问题,给出了一种对邻近干扰源带来的干扰空间影响范围的变化的分析方法,并系统分析了存在邻近干扰源情况下干扰影响空间范围的变化情况。为克服存在邻近干扰源时干扰影响空间范围影响因素多,部分影响因素还存在一定耦合性的难题,首先在空间指定位置机载GNSS接收机接收干扰信号功率建模的基础上,分别计算主干扰源周围不存在和存在邻近干扰源这两种情况下空间中不同位置的干扰信号接收功率;其次分析存在邻近干扰源时干扰空间范围的影响因素及这些影响因素的具体影响;最后在曲线间改进豪斯多夫距离的基础上,提出边界差异均值和边界差异标准差两个定量指标,定量化评估存在邻近干扰源时与无邻近干扰源时相比不同参数下干扰空间范围的差异。通过这一定量化评估,可以获知在什么情况下存在邻近干扰源时对干扰空间范围影响小,在这些情况下使用单干扰源假设的定位分析模型不会对干扰源定位结果产生大的影响;以及在什么情况下存在邻近干扰源时会使干扰空间范围产生显著变化,在这些情况下再使用单干扰源假设的定位分析方法干扰源定位结果将可能产生显著偏差。实验结果表明,在干扰源天线方向图确定的情况下,邻近干扰源与主干扰源的功率比、主干扰源的功率值、干扰源间距和分析高度层都会对空间干扰范围产生影响;实验分析结果通过边界差异均值和边界差异标准差给出了主干扰源周围不存在和存在邻近干扰源这两种情况下干扰影响空间范围差异小的条件。实验结果中不同条件空间影响范围的特点以及干扰影响空间范围差异小的条件,可以指导可能存在多个干扰源的场景下干扰源的有效定位与排查。Abstract: The Global Navigation Satellite System (GNSS) has been widely used over the past few decades. However, various new types of radio interference sources have emerged, affecting the robustness of GNSS application systems. GNSS plays an increasingly important role in civil aviation operations. Research on localizing interference sources affecting GNSS signals used by civil aviation aircraft has been conducted both domestically and internationally. Existing studies often assume that only a single interference source exists within the analysis space. In reality, there are instances where multiple GNSS interference sources exist near an interference source that disrupts civil aviation GNSS signals. Analyzing the spatial boundaries affected by these interference sources is crucial for guiding the localization and effective elimination of interference. In this paper, we address the influence of adjacent GNSS interference sources on the interference spatial range of a primary GNSS interference source under various parameters. We propose a method to analyze the differences in interference spatial ranges caused by nearby interference sources and systematically analyze the variations in these ranges. A significant difficulty in this analysis is that the interference spatial range is influenced by numerous factors, some of which are interrelated. Firstly, we model the received interference signal power of an airborne GNSS receiver at a specific position, calculating the received power in different spatial positions both with and without the presence of an adjacent interference source. Secondly, we analyze the influencing factors on the interference spatial range and their specific impacts. Finally, we introduce two quantitative indices based on the modified Hausdorff distance between the curves: the mean value of boundary difference and the standard deviation of boundary difference. These indices are used to quantitatively evaluate the differences in interference spatial range under varying conditions, with or without the presence of adjacent interference sources. Through quantitative evaluation, we identify scenarios where the presence of adjacent interference sources minimally impacts the interference spatial range and where localization results derived from models that assume a single interference source may incur little bias. Conversely, we also determine situations where adjacent interference sources significantly influence the interference spatial range, potentially leading to considerable errors in localization results based on single interference source assumptions. Experimental results show that when the antenna pattern of the interference source is established, the power ratio of the adjacent interference source to the main source, the power level of the main interference source, the distance between the sources, and the analysis height layer all affect the interference spatial range. Our analysis provides criteria for lesser boundary differences in the interference spatial range with and without adjacent interference sources surrounding the primary interference source, utilizing the mean and standard deviation of boundary differences. The findings regarding the characteristics of the interference spatial range under various conditions and instances of lesser boundary differences can assist in effectively localizing and eliminating interference sources in scenarios where multiple interference sources are present.