The output SNR analysis of Parameter Estimation Algorithm for QFM Signals using Ambiguity function based on the Linear Canonical Transform

The parameter estimation algorithm for QFM signals using ambiguity function based on the linear canonical transform (LCTAF) is simple and has high estimation precision. Its error transfer is also small and has a good application prospect in the project. In this paper, the output signal-to-noise ratio (SNR) of this algorithm is analyzed deeply and derived the relationship between the output SNR and sampling points and input SNR. By simulation experiments we compare the output SNR of LCTAF algorithm, with that of integrated generalized ambiguity function (IGAF) algorithm and polynomial-phase transform (PPT) algorithm under the same condition. And the required sampling points when the three algorithms achieve the same output SNR are also presented. We find out that the output SNR of our proposed algorithm is bigger than that of IGAF algorithm and PPT algorithm under the same simulation conditions. When achieving the same output SNR, the sampling points of LCTAF needed are 1/4 and 1/9 of the IGAF algorithm and the PPT algorithm needed, respectively. That is, the LCTAF algorithm needs fewer sampling points than IGAF algorithm and PPT algorithm.