自适应融合IMM的自主无线电载波跟踪算法

针对深空自主无线电接收技术中信噪比(SNR)未知的载波跟踪问题，提出了一种自适应融合的交互式多模型(IMM)算法，可以根据实际环境的信噪比自适应地调节IMM估计器的噪声方差，以实现对未知SNR信号的正常跟踪，并保证频率跟踪精度几乎不受初始噪声方差的影响。在分析系统收敛性的基础上，该算法采用模型概率自适应调整策略，根据系统收敛判断条件自适应地调整IMM模型集中各模型的概率，保证了系统的收敛性，提高了跟踪精度，与广泛使用的Sage-Husa自适应滤波算法相比，收敛时间缩短了一倍左右。

An Adaptive Fusion IMM Algorithm for Autonomous Radio Carrier Tracking

An adaptive fusion algorithm based on interactive multiple model (IMM) is developed to track the signal with unknown signal to noise ratio (SNR) information for the technique of deep space autonomous radio receivers. The new algorithm if used to adjust the noise variances of the IMM estimator adaptively according to the SNR of the actual environment, so that it can normally track the signal with unknown SNR information and ensure that the frequency tracking accuracy is almost not affected by initial noise variances. Based on the analysis of the system convergence attribute, an adaptive adjustment strategy of model probabilities is adopted to adjust the probability of each sub module in the IMM system adaptively according to the system convergence criterion. By doing this, the new algorithm introduced here ensures the system convergence and improves the tracking accuracy. The convergence time of the adaptive fusion IMM algorithm is shorter by a half than that of Sage-Husa adaptive filter algorithm used widely.