自适应强跟踪Sage-Husa卡尔曼滤波器载波环设计

Design of Carrier Tracking Loop Based on Adaptive Strong Tracking Sage-Husa Kalman Filter

针对Sage-Husa自适应卡尔曼滤波算法易引起发散且对初始条件的选取非常敏感的问题,提出一种自适应强跟踪Sage-Husa滤波算法。该算法从Sage-Husa自适应卡尔曼滤波算法出发,引入强跟踪技术,通过渐消因子在线修正一步预测误差协方差矩阵,使算法具有应对场景变化等不确定情况的能力,增强算法的鲁棒性;通过改进Sage-Husa自适应算法对噪声方差阵进行实时在线估计,使算法具有应对噪声变化的自适应能力,保证较好的跟踪精度。仿真结果表明,所提出的滤波算法能够有效提高载波环路的跟踪精度和鲁棒性。

Aiming at the problem that the Sage-Husa adaptive Kalman filter algorithm is easy to diverge and is sensitive to the selection of initial conditions an adaptive strong tracking algorithm based on Sage-Husa adaptive Kalman filter algorithm is proposed.The algorithm starts from the Sage-Husa adaptive Kalman filter algorithm and introduces the strong tracking technology in it.By online modifying the one-step prediction error covariance matrix with fading factor the algorithm can cope with the uncertainties such as scene changes and has stronger robustness.By improving the Sage-Husa adaptive algorithm the noise covariance matrix is estimated online in real time so that the algorithm is adaptive to noise changes and has better tracking accuracy.Simulation results show that the proposed filter method can effectively improve the tracking accuracy and robustness of the carrier tracking loop.