机动目标模型匹配卡尔曼滤波载波跟踪算法

针对传统卡尔曼滤波器用于高动态载波跟踪时性能不够理想的问题，提出一种基于机动目标模型匹配的卡尔曼滤波载波跟踪算法，能够在载波参数剧烈变化的条件下实现稳定的载波同步。所提算法较传统算法更加契合实际环境，具有实用价值高、应用范围广等优点。使用线性卡尔曼滤波器，无需矩阵求逆运算，计算复杂度低，便于工程实现。仿真结果表明，所提算法在跟踪具有剧烈动态特性的载体信号时能够显著提高跟踪精度，且跟踪门限信噪比能够降低约3 dB。

Kalman filter used for carrier tracking algorithm based on matched maneuvering target model

For the problem then the traditional Kalman filter does not perform well enough when it is used in high dynamic carrier tracking, an improved Kalman filter based on the matched maneuvering target model is proposed, which could achieve stable carrier synchronization under high dynamic conditions. Compared with traditional algorithms, the proposed algorithm is more realistic, with high practical value, and a wide range of applications. Moreover, the tracking system uses a linear Kalman filter without matrix inverse operation, which has low computational complexity and is easy to implement in the real system. Simulation results show that the proposed algorithm is more suitable for the high dynamic environment, which could significantly improve the tracking accuracy and reduce about 3 dB of the tracking threshold on signal-to-noise ratio.