软件接收机中基于数据处理的多径估计方法

当信噪比(signal-to-noise ratio, SNR）较低时基于数据处理的多径估计算法的估计性能显著降低。提出了基于Kalman滤波和Teager-Kaiser (TK）算子/最小二乘(least square, LS）相结合的多径估计算法，简称KTK/KLS算法。该算法通过Kalman滤波消除低SNR的高斯噪声对相关输出的影响，然后将滤波后的相关输出用于TK算子/LS估计直接信号时间延迟或多径参数。KTK/KLS算法有效解决了仅使用TK算子和LS算法进行参数估计时对噪声比较敏感的问题，保留了二者对多径比较敏感的优点。最后，通过仿真将KTK/KLS算法与其他高效的基于数据处理的多径估计算法进行比较，结果表明所提出算法的多径估计精度优于对比算法。

Multipath estimation algorithms based on data processing in software receiver

For multipath estimation algorithms based on data processing, their performances degrade dramatically in Gaussian noise environments with low signal-to-noise ratio (SNR）. Thus, a hybrid algorithm of Kalman filter and Teager-Kaiser (TK） operator/least square (LS） is presented for multipath estimation in the Gaussian noise environments with a low SNR, i.e., KTK/KLS algorithm. For the proposed algorithm, Kalman filter is used to remove the influence of Gaussian noise, and the TK operator is used for the estimation of direct signal time delay or LS algorithm for the estimation of multipath parameters. KTK/KLS algorithm can solve the problem that TK and LS are sensitive to noise and retain the advantage that TK and LS are sensitive to multipath. Furthermore, KTK and KLS algorithms are compared with other high efficient multipath estimation algorithms by simulation. The results show that the proposed algorithm has a higher estimation accuracy than the compared algorithms.