Particle filter-based real-time phase line bias estimation for GNSS-based attitude determination with common-clock receivers

Wu, Mingkui; He, Yan; Luo, Shuai; Liu, Wanke

doi:10.1016/j.asr.2022.09.062

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…To address these problems, the KF (Kalman filter) is implemented in the carrier tracking loop because of its robustness in tracking performance [4]. The KF technique has been employed in many areas, such as navigation [5,6], satellite attitude estimation [7,8], unmanned aerial vehicles [9], high accuracy positioning [10], and so on. Psiaki and Jung [11] designed a KF-based tracking loop that can enhance the tracking sensitivity compared with PLL.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter with Adaptive Covariance Estimation for Carrier Tracking under Weak Signals and Dynamic Conditions

Cheng,

Zhang,

Wang

et al. 2024

Electronics

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Kalman filtering (KF)-based tracking has been commonly employed in global navigation satellite system (GNSS) receivers to achieve robust tracking. However, under more serious conditions, such as severe strength attenuation and abrupt dynamic coexisting environments, it is difficult for KF-based tracking to keep tracking well due to the fixed noise statistics. To further enhance the carrier tracking performance, this paper proposes an adaptive KF carrier tracking method for resisting signal strength fading and high dynamic environments. The proposed method introduces the adaptive factor to adjust the process noise covariance to accommodate the noise statistics in actual variable situations. Moreover, we apply the chi-square hypothesis test to detect system stability. The adaptive factor is only applied when the system is not stable, which can enhance computational efficiency. The proposed method is conducted in the GPS L1 software receivers. According to the results, the proposed algorithm can improve the robustness in tracking performance compared with other tracking methods under signal serious fading and high dynamic conditions. Using the proposed method, GNSS receivers’ navigation performance can be improved under complex conditions.

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