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Frequency Tracking and Mitigation Method of Multiple GNSS Interferences Using an Adaptive Linear Kalman Notch Filter

机译:使用自适应线性Kalman Notch滤波器的多个GNSS干扰的频率跟踪和缓解方法

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In this paper, a GPS interference frequency tracking and mitigation method using an adaptive linear Kalman notch filter (LKNF) is proposed to track and mitigate GPS interference signal. This LKNF has faster convergence rate than the conventional notch filtering method, and it has smaller signal loss than the conventional notch filter. Furthermore, the LKNF is more robust because it uses estimates as well as measurements. However, in order to use this method, we need to know interference frequency to estimate. Thus, time-frequency analysis method based on signal energy distribution is used. After applying this algorithm, we also implemented a multiple LKNF which is a Kalman filter with augmented states to estimate and remove multiple interference signals. In addition, in order to adjust the notch depth according to the power of the jamming signal, an adaptation logic is designed. The adaptive logic for adjusting the notch depth using signal/noise contents is based on the Q-adaptation method of adaptive Kalman filtering for indirectly adjusting a Kalman gain, which is guaranteed to be nonnegative. In order to analyze the performance of the proposed method, three Matlab-based simulations were performed. The frequency tracking and mitigation performance is compared with a conventional lattice IIR notch filter and LKNF without adaptive logic. Simulation results show that the proposed tracking and mitigation algorithm can efficiently track and eliminate interference well compared with the conventional methods.
机译:在本文中,提出了使用自适应线性卡尔曼缺口滤波器(LKNF)的GPS干扰频率跟踪和缓解方法来跟踪和减轻GPS干扰信号。该LKNF具有比传统的Notch滤波方法更快的收敛速度,并且它具有比传统的缺口滤波器更小的信号损耗。此外,LKNF更强大,因为它使用估计和测量值。但是,为了使用此方法,我们需要知道干扰频率估计。因此,使用基于信号能量分布的时频分析方法。在应用此算法之后,我们还实现了一个多LKNF,该LKNF是具有增强状态的卡尔曼滤波器来估计和去除多个干扰信号。另外,为了根据干扰信号的功率调整凹口深度,设计适应逻辑。用于使用信号/噪声内容调整凹口深度的自适应逻辑基于自适应Kalman滤波的Q适应方法,用于间接调整卡尔曼增益,这保证是非负的。为了分析所提出的方法的性能,进行了三种基于MATLAB的模拟。将频率跟踪和缓解性能与传统的格子IIR Notch滤波器和LKNF进行比较,没有自适应逻辑。仿真结果表明,与传统方法相比,所提出的跟踪和缓解算法可以有效地跟踪和消除干扰。

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