【摘要】 在雷达、车载等动态协同组网系统中，高精度时间同步是该系统正常工作的基本条件。但是在动态组网系统或者低截获场景下，时间比对信号强度弱，并处于动态场景，此时时间同步系统鲁棒性差、同步精度低。因此，需要提高时间同步系统在复杂的动态组网系统下的时间同步精度。调制解调器是双向时间比对系统的核心设备，而跟踪环路是其中关键部分。复杂场景下跟踪环路很容易失锁，为了提高跟踪环路鲁棒性，该文提出一种基于自适应卡尔曼滤波(AKF)的跟踪算法。该算法引入自适应因子来调节系统噪声协方差矩阵，从而应对外部变化的输入信号。试验结果显示，与传统锁相环跟踪环路(PLL)和标准卡尔曼滤波跟踪环相比，在弱信号和动态信号同时存在时该算法跟踪鲁棒性和自适应性更好，并且算法复杂度不高。该算法对于提高动态协同组网系统的时间同步精度具有重要意义。更多还原

【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.更多还原