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A Robust Method to Suppress Jamming for GNSS Array Antenna Based on Reconstruction of Sample Covariance Matrix

机译:基于样本协方差矩阵重构的鲁棒抑制GNSS阵列天线干扰的方法

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摘要

The Global Navigation Satellite System (GNSS) receiver is vulnerable to active jamming, which results in imprecise positioning. Therefore, antijamming performance of the receiver is always the key to studies of satellite navigation system. In antijamming application of satellite navigation system, if active jamming is received fromarray antennamain-lobe, main-lobe distortion happens when the adaptive filtering algorithm forms main-lobe nulling. A robust method to suppress jamming for satellite navigation by reconstructing sample covariance matrix without main-lobe nulling is proposed in this paper. No nulling is formed while suppressing the main-lobe jamming, which avoids main-lobe direction distortion. Meanwhile, along with adaptive pattern control (APC), the adaptive pattern of array antenna approaches the pattern without jamming so as to receive the matching navigation signal. Theoretical analysis and numerical simulation prove that this method suppresses jamming without main-beam distortion. Furthermore, the output SINR will not decrease with the main-lobe distortion by this method, which improves the antijamming performance.
机译:全球导航卫星系统(GNSS)接收器容易受到主动干扰,导致定位不准确。因此,接收机的抗干扰性能一直是研究卫星导航系统的关键。在卫星导航系统的抗干扰应用中,如果从阵列天线主瓣接收到有源干扰,则当自适应滤波算法形成主瓣置零时,主瓣失真就会发生。提出了一种鲁棒的方法,通过重构样本协方差矩阵而没有主瓣零点抑制卫星导航的干扰。在抑制主瓣干扰时不会形成零点,从而避免了主瓣方向失真。同时,与自适应模式控制(APC)一起,阵列天线的自适应模式接近该模式而不会被干扰,从而接收匹配的导航信号。理论分析和数值模拟证明,该方法抑制了干扰,且没有主光束失真。此外,通过该方法,输出SINR不会随着主瓣失真而降低,从而提高了抗干扰性能。

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  • 来源
    《International journal of antennas and propagation》 |2017年第1期|9764283.1-9764283.12|共12页
  • 作者单位

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

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