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Research of GNSS Spoofer Localization Using Information Fusion Based on Particle Filter

机译:基于粒子滤波器的信息融合GNSS对扬声器定位研究

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A spoofer localization algorithm using information fusion based on the particle filter (PF) is proposed. There are many researches about the jammer localization, but less researches about the spoofer localization. The jamming signal, whose structure is unknown to us, usually is above the thermal noise. According to the effect of jamming signal bandwidth on the measuring of time and frequency, we cannot obtain the precise measurements of TDOA and FDOA simultaneously. Thus, we usually cannot implement the information fusion of time and frequency in the jammer localization. Besides, we cannot locate the spoofer directly with the method of jammer localization, because the spoofing signal is usually below the noise. However, we can obtain the precise measurements of time and frequency of the spoofing signal at the same time because the spoofing signal needs to disguise as the true GNSS signal. Based on this, we propose a localization algorithm using the information fusion of TDOA and FDOA. We couple the position and velocity together by the PF, which will not introduce the linearization error. By the analysis of the CRLB, we find the information fusion can improve the system performance. To verify the algorithm, we simulate and analyze the results of PF and the weighted least squares (WLS). Compared to the WLS without information fusion, the PF has a better performance of locating and tracking, which can work properly under the circumstances where the spatial layout is poor, the number of base stations is small and the measuring errors are large.
机译:提出了一种基于粒子滤波器(PF)的信息融合的解码器定位算法。关于干扰定位有很多研究,但对解释器本地化的研究较少。其结构未知的干扰信号通常高于热噪声。根据干扰信号带宽对时间和频率测量的影响,我们不能同时获得TDOA和FDOA的精确测量。因此,我们通常无法在干扰定位中实现时间和频率的信息融合。此外,我们不能直接用干扰定位方法定位解波器,因为欺骗信号通常低于噪声。然而,我们可以同时获得欺骗信号的时间和频率的精确测量,因为欺骗信号需要伪装为真正的GNSS信号。基于此,我们使用TDOA和FDOA的信息融合提出了一种定位算法。我们将位置和速度耦合在一起,PF将不会引入线性化误差。通过分析CRLB,我们发现信息融合可以提高系统性能。为了验证算法,我们模拟并分析PF和加权最小二乘(WLS)的结果。与没有信息融合的WLS相比,PF具有更好的定位和跟踪性能,可以在空间布局差的情况下正常工作,基站的数量很小,测量误差大。

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