GPS接收机干扰抑制技术研究
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摘要
全球导航卫星系统可以用来测量运动载体的速度、三维位置和时间。随着它不断地完善和发展,以及未来跟互联网和移动网结合,它将在军用和民用领域获得更加广泛地应用。导航信号非常微弱,很容易遭到各种潜在的无意干扰和敌方的恶意干扰,因此如何保证导航系统在干扰环境下安全、可靠工作成为一个重要问题。导航系统的干扰主要是针对地面接收设备,它分为压制式干扰和欺骗式干扰,本文主要研究了导航信号中调频干扰和宽带噪声干扰这两类压制式干扰的抑制问题,内容如下:
首先针对单传感器接收机,研究了线性调频干扰的抑制问题。通过分析Wigner-Ville分布、Radon-Wigner变换和分数阶傅立叶变换的关系,提出了在变换域消除线性调频干扰方案,同时由于分数阶傅立叶变换对信号线性处理,接收信号在变换域上不会产生如Wigner-Ville分布一样的交叉干扰。
其次针对阵列信号处理,研究了宽带噪声干扰抑制问题。阵列信号处理通常利用期望信号的来波方向,根据一定的最优准则,完成波束成形,抑制干扰,增强已知信号。我们研究了在无任何导航信号先验信息情况下,通过正交投影和最大信噪比技术得到最优的加权向量,完成波束成形,抑制干扰和加权期望方向的来波信号。
然后对于阵列信号处理,研究了在宽带噪声干扰下信源的参数估计问题。在阵列信号处理中,信源个数和信号来波方向是阵列信号处理的两个重要参数。一般的信源参数估计都是在噪声环境下进行的,在强干扰环境,这些方法会失效。我们提出了使用阵列子空间技术抑制接收信号中的强干扰,然后利用稳健的变换Gerschgorin方法估计信源的个数和高分辨率的MUSIC方法估计信号的来波方向。
最后针对阵列信号处理,研究了干扰变场景变化时,怎样快速获得最优的加权向量,自适应跟踪干扰并消除它。根据期望信号来波方向和RLS算法快速跟踪未知参数变化的能力,提出了基于最小输出能量准则的RLS算法抑制干扰,导出了所提出的RLS算法步骤,分析了算法的稳态性能和动态跟踪能力。
Global Navigation Satellite System (GNSS) is commonly deployed to measure the speed of a moving object and estimate its three-dimension position and time. Through increasing development and perfection, GNSS will be combined with internet and mobile network in the future and therefore applied to military and civilian areas widely. However, the GNSS signals are easily subject to intentional and malicious interference because they are inherently weak. How to guarantee the reliable and safe operation of GNSS in the jamming becomes more and more important. The interference for GNSS is mainly targeted at receivers on the earth and can be classified into overwhelming interference and spurious interference. In this thesis, we consider the issue of suppressing two kinds of overwhelming interference, i.e., FM interferer and wideband noise interferer on navigation receivers in the following aspects.
First, we analyze the relationships of the Wigner-Ville distribution, Radon-Wigner transform and the fractional Fourier transform, and identify the feature of Linear Frequency Modulation (LFM) interference which is different from the navigation satellite signals’in the transform domain for a receiver with a sensor. Based on these observations, we propose a new approach to cancel LFM interference in the transform domain and, meanwhile, avoid any cross-term interference in the form of Wigner-Ville distribution.
Second, in array signal processing we consider how to estimate the optimal weight vector in the strong jamming environment when any prior information of navigation signals is unknown. To solve this issue, we adopt the orthogonal projection and maximum signal-noise ratio technique to achieve the optimum weight vector, implement beamforming, and suppress interference and enhance desired signal.
Third, the number of sources and the direction of arrival (DOA) of each source are two important parameters in array signal processing. The estimation of source parameters is usually executed under noise environment. Hence, the conventional approaches fail in the strong jamming case. In this thesis, we apply a combining technique to estimate the source parameters. More specifically, we use subspace technique to suppress interference, adopt transform Gerschgorin approach to exploit the received signal after interference cancellation for estimating the number of desired signals, and utilize the high resolution MUSIC method to identify the DOA of each desired signal.
Finally, in the array signal processing, we investigate how to adapt a weight vector quickly for a changing jamming environment. In view of the excellent capability of the RLS algorithm in tracking an unknown parameter vector, the present thesis proposes an improving RLS-version algorithm based on the minimum output energy, derives the steps, and demonstrates its steady-state performance and tracking capability.
首先针对单传感器接收机,研究了线性调频干扰的抑制问题。通过分析Wigner-Ville分布、Radon-Wigner变换和分数阶傅立叶变换的关系,提出了在变换域消除线性调频干扰方案,同时由于分数阶傅立叶变换对信号线性处理,接收信号在变换域上不会产生如Wigner-Ville分布一样的交叉干扰。
其次针对阵列信号处理,研究了宽带噪声干扰抑制问题。阵列信号处理通常利用期望信号的来波方向,根据一定的最优准则,完成波束成形,抑制干扰,增强已知信号。我们研究了在无任何导航信号先验信息情况下,通过正交投影和最大信噪比技术得到最优的加权向量,完成波束成形,抑制干扰和加权期望方向的来波信号。
然后对于阵列信号处理,研究了在宽带噪声干扰下信源的参数估计问题。在阵列信号处理中,信源个数和信号来波方向是阵列信号处理的两个重要参数。一般的信源参数估计都是在噪声环境下进行的,在强干扰环境,这些方法会失效。我们提出了使用阵列子空间技术抑制接收信号中的强干扰,然后利用稳健的变换Gerschgorin方法估计信源的个数和高分辨率的MUSIC方法估计信号的来波方向。
最后针对阵列信号处理,研究了干扰变场景变化时,怎样快速获得最优的加权向量,自适应跟踪干扰并消除它。根据期望信号来波方向和RLS算法快速跟踪未知参数变化的能力,提出了基于最小输出能量准则的RLS算法抑制干扰,导出了所提出的RLS算法步骤,分析了算法的稳态性能和动态跟踪能力。
Global Navigation Satellite System (GNSS) is commonly deployed to measure the speed of a moving object and estimate its three-dimension position and time. Through increasing development and perfection, GNSS will be combined with internet and mobile network in the future and therefore applied to military and civilian areas widely. However, the GNSS signals are easily subject to intentional and malicious interference because they are inherently weak. How to guarantee the reliable and safe operation of GNSS in the jamming becomes more and more important. The interference for GNSS is mainly targeted at receivers on the earth and can be classified into overwhelming interference and spurious interference. In this thesis, we consider the issue of suppressing two kinds of overwhelming interference, i.e., FM interferer and wideband noise interferer on navigation receivers in the following aspects.
First, we analyze the relationships of the Wigner-Ville distribution, Radon-Wigner transform and the fractional Fourier transform, and identify the feature of Linear Frequency Modulation (LFM) interference which is different from the navigation satellite signals’in the transform domain for a receiver with a sensor. Based on these observations, we propose a new approach to cancel LFM interference in the transform domain and, meanwhile, avoid any cross-term interference in the form of Wigner-Ville distribution.
Second, in array signal processing we consider how to estimate the optimal weight vector in the strong jamming environment when any prior information of navigation signals is unknown. To solve this issue, we adopt the orthogonal projection and maximum signal-noise ratio technique to achieve the optimum weight vector, implement beamforming, and suppress interference and enhance desired signal.
Third, the number of sources and the direction of arrival (DOA) of each source are two important parameters in array signal processing. The estimation of source parameters is usually executed under noise environment. Hence, the conventional approaches fail in the strong jamming case. In this thesis, we apply a combining technique to estimate the source parameters. More specifically, we use subspace technique to suppress interference, adopt transform Gerschgorin approach to exploit the received signal after interference cancellation for estimating the number of desired signals, and utilize the high resolution MUSIC method to identify the DOA of each desired signal.
Finally, in the array signal processing, we investigate how to adapt a weight vector quickly for a changing jamming environment. In view of the excellent capability of the RLS algorithm in tracking an unknown parameter vector, the present thesis proposes an improving RLS-version algorithm based on the minimum output energy, derives the steps, and demonstrates its steady-state performance and tracking capability.
引文
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