Abstract:

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.