A Low Complexity Algorithm for Time-Frequency Joint Estimation of CAF Based on Numerical Fitting
Zhengyu Zhang, Xin Wang, Yong-Qing Zou, Renfei Zhang · 2020
In order to reduce the complexity of cross ambiguity function (CAF), this paper proposes a low-complexity time-frequency joint estimation algorithm based on numerical fitting for CAF. The algorithm makes full use of the property that CAF is symmetrical in the frequency domain. Firstly, the CAF is used to perform time-frequency joint rough estimation. In order to meet the frequency estimation accuracy requirement, the radial basis function (RBF) method is used to estimate the frequency difference near the frequency difference estimation after delay compensation. On the one hand, do time-frequency joint rough estimation can greatly reduce the complexity of the search based on CAF, then sampling the CAF and using RBF for numerical fitting near the result of rough estimation of frequency, and obtaining the frequency difference through can also reduce the complexity of the search; on the other hand, compared to existing algorithms, the methods based on numerical fitting can reduce the priori information needed for estimation, avoid long-term observations and complex pre-level operations. The simulation results show that compared with the method which searches the peak of CAF, the proposed algorithm can greatly reduce the complexity while satisfying the accuracy requirements of time-frequency joint estimation.