A Time Transfer Tracking Loop Based on Adaptive Kalman Filter in Complex Conditions

CHENG Yan; WANG Haifeng; WANG Xueyun; GUO Liang; ZHANG Shengkang; GE Jun

doi:10.11999/JEIT230813

Volume 45 Issue 11

Nov. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(11): 4110-4116

CHENG Yan, WANG Haifeng, WANG Xueyun, GUO Liang, ZHANG Shengkang, GE Jun. A Time Transfer Tracking Loop Based on Adaptive Kalman Filter in Complex Conditions[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4110-4116. doi: 10.11999/JEIT230813

Citation:

CHENG Yan, WANG Haifeng, WANG Xueyun, GUO Liang, ZHANG Shengkang, GE Jun. A Time Transfer Tracking Loop Based on Adaptive Kalman Filter in Complex Conditions[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4110-4116. doi: 10.11999/JEIT230813

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A Time Transfer Tracking Loop Based on Adaptive Kalman Filter in Complex Conditions

doi: 10.11999/JEIT230813 cstr: 32379.14.JEIT230813

Science and Technology on Metrology and Calibration Laboratory, Beijing Institute of Radio Metrology and Measurement, Beijing 100854, China

Received Date: 2023-08-01
Rev Recd Date: 2023-10-12

Available Online: 2023-10-20

Publish Date: 2023-11-28

Abstract

Abstract

In dynamic collaborative networking systems such as radar and vehicular network systems, high precision time synchronization is a basic condition for the normal operation of these systems. However, in dynamic network systems and low interception scenarios, the time transfer signal is weak and dynamic simultaneously, and thus the time synchronization system has poor robustness and synchronization accuracy. Accordingly, it is necessary to improve the time synchronization accuracy in complex dynamic networking systems. The time transfer modem is the core device of the two-way time transfer system, and the tracking loop is a key part of it. The tracking loop can easily lose lock in complex conditions. To improve the robustness of the tracking loop, an Adaptive Kalman Filter (AKF) tracking algorithm is proposed. This tracking loop employs the adaptive factor to adjust the system noise covariance matrices to adapt to the variable input signal. The test results show that, compared with the traditional Phase Lock Loop (PLL) tracking method and the standard KF tracking loop, the proposed tracking loop shows better robustness and adaptability under weak signal and dynamic conditions. Moreover, the computational complexity of the proposed algorithm is not high. This algorithm is of great significance for improving the time synchronization accuracy of complex dynamic collaborative networking systems.

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References(16)

References

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