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State estimation for multiple clocks under anomalies using l(1)-norm optimization

机译:使用L(1)-norm优化的异常下多个时钟的状态估计

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摘要

We investigate an estimation strategy for multiple clock systems, which is in particular resilient against clock anomalies. The clock anomalies, including frequency or phase jumps, can potentially degrade the estimation performance for multiple clock systems. While the conventional Kalman filter is known as one of the powerful methods for the estimation problem of multiple clock systems, we will show that it is vulnerable to the effects of clock anomalies. In this paper, we overcome this issue by employing an alternative approach to the Kahnan filter, which is based on solving the l(1)-norm optimization problem. The estimation method employs a multiple clock model that includes only phase states. Nevertheless, the approach is shown to have more resilience against clock anomalies than the Kalman filter, because the deviation of the state from the apriori estimate is evaluated based on the l(1)-norm instead of the l(1)-norm. Moreover, the approach is shown to be competitive with many reset algorithms, since it does neighter require special stability assumptions about the reference clock, nor require any threshold for detecting the anomalies. The usefulness of the proposed method is validated through several numerical examples using actual clock data.
机译:我们调查多个时钟系统的估计策略,特别是针对时钟异常的弹性。时钟异常(包括频率或跳频)可能会降低多个时钟系统的估计性能。虽然传统的卡尔曼滤波器被称为多个时钟系统估计问题的强大方法之一,但我们将显示它易受时钟异常的影响。在本文中,我们通过采用Kahnan滤波器的替代方法来克服这个问题,这是基于解决L(1)-norm优化问题。估计方法采用多个时钟模型,该模型仅包括相位状态。然而,该方法被示出比卡尔曼滤波器对时钟异常具有更多的韧性,因为基于L(1)-norm而不是L(1)-norm来评估状态来自APRiori估计的状态的偏差。此外,该方法被证明具有许多复位算法具有竞争力,因为它确实需要关于参考时钟的特殊稳定性假设,也不需要检测异常的任何阈值。通过使用实际时钟数据通过几个数字示例验证所提出的方法的有用性。

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