一种基于数据和导频信道联合利用的GPS L2C信号跟踪方法

Yucheng Liu, Hong Li, X. Cui, Mingquan Lu
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引用次数: 2

摘要

GPS L2C信号是现代化的GPS民用信号之一。相对于GPS L1 C/A信号,L2C信号在恶劣环境下具有更强的鲁棒性,利用了新设计的导频信道,其跟踪阈值更低。到目前为止,现有的一些跟踪方法只基于L2C信号的导频信道或数据信道,而抛弃了其他信道,导致50%的功率损耗。其他方法是基于数据输出和导频信道鉴别器的联合跟踪,这种方法需要更多的额外计算资源,并且跟踪的集成时间受数据的限制。本文提出了一种基于数据和导频信道联合跟踪而非鉴别器的新方法。我们知道GPS信号的导航数据主要由星历和历书组成,是连续反复广播的。因此GPS信号的导航数据是可预测的。在此基础上,该方法通过设计新的导航数据预测模块对数据信道的导航数据进行去除,然后将数据与导频信道的集成结果进行相干组合。这样,相干积分结果同时具有数据信道和导频信道的能量。与以往的方法相比,该方法无功耗损失,且不需要额外的计算资源。此外,集成时间不会受到导航数据的限制,因为它们已被可预测地删除,并且期望有更好的跟踪性能。理论和仿真结果验证了上述结果。
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A tracking method for GPS L2C signal based on the joint using of data and pilot channels
GPS L2C signal is one of the modernized GPS civilian signals. With respect to GPS L1 C/A signal, L2C signal is more robust in harsh environment, since its tracking threshold would be lower by taking the advantage of new designed pilot channel. Up to now, some of current tracking methods are only based on the pilot channel or data channel of L2C signal and discard the other, which results in 50% power loss. Other methods are based on the joint tracking of the outputs of data and pilot channels' discriminators, which needs more additional computation resources and the integration time of tracking is limited by data. In this paper, a new method based on joint tracking of data and pilot channels but not discriminators is proposed. We know the navigation data of GPS signal, mainly consisted of ephemeris and almanac, are continuously and repeatedly broadcast. So the navigation data of GPS signal are predicable. Based on this, the proposed method removes the navigation data of data channel through a new designed navigation data predication module, before coherently combines the integration results of data and pilot channel. Then, the coherent integration result has both the energy of data channel and pilot channel. Compared with the previous methods, the proposed method has no power loss and it doesn't need additional computation resource. Furthermore, the integration time will not be limited by navigation data since they have been predictably removed and much better tracking performance is expected. Theoretical and simulation results demonstrated the results.
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