GNSS干扰干扰的协同/分布式定位与表征

M. Bartolucci, R. Casile, G. Corazza, A. Durante, Giulio Gabelli, A. Guidotti
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引用次数: 6

摘要

干扰是对导航系统完整性的主要威胁。在本文中,我们提供了在协作和分布式架构下GNSS干扰信号定位和表征操作的新方法。在定位方面,利用差分接收信号强度(DRSS)和扩展卡尔曼滤波器(EKF)。关于特征,设想了两种方法,都基于利用karhunen - lo扩展(KLE)。首先,节点估计和重建接收到的干扰信号,并在它们之间或与融合中心(FC)共享这些数据,融合中心利用KLE进行数据聚合。在第二种方法中,信号估计和重建在FC进行,从而降低了节点的复杂性。分别从平均定位误差(Mean localization Error, MLE)和均方误差(Mean Square Error, MSE)两方面分析了定位和表征算法的性能。仿真结果表明,该算法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative/distributed localization and characterization of GNSS jamming interference
Interference is the major threat to integrity of navigation systems. In this paper, we provide new approaches to localization and characterization operations of GNSS jamming signals in both cooperative and distributed architectures. As for localization, Differential Received Signal Strength (DRSS) and an Extended Kalman Filter (EKF) are exploited. With respect to characterization, two approaches are envisaged, both based on exploiting the Karhunen-Loève Expansion (KLE). In the first, the nodes estimate and reconstruct the received jamming signal and share this data between themselves or with a Fusion Center (FC), which exploits KLE for data aggregation. In the second approach, signal estimation and reconstruction are performed at the FC, thus decreasing the nodes' complexity. The performance of the localization and characterization algorithms are analyzed in terms of Mean Localization Error (MLE) and Mean Square Error (MSE), respectively. Simulation results show that the proposed algorithms provide good performance.
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