Efficient Coefficient Estimation of Polynomial Phase Signals: A Novel FFT-Free Filtering Method

The polynomial phase signal (PPS) model is widely utilized in various communication systems, with the estimation of its phase polynomial coefficients being a primary objective for parameter estimation. Noise is a significant source of error in PPS coefficient estimation; therefore, enhancing the signal-to-noise ratio (SNR) of PPSs is crucial for achieving more accurate coefficient estimates. This paper presents a novel filtering algorithm for PPSs that leverages sub-signal filtering and combination techniques. The sub-signal filtering employs a fast, FFT-free frequency estimation method based on phase difference averaging (PDA), de-chirping, and a moving average filter. Following sub-signal filtering, the combination of sub-signals through averaging in overlapping regions further improves the SNR. Also, for selection of optimal sub-signal length, a new optimality criterion is proposed. Theoretical analyses and simulations demonstrate that this algorithm achieves acceptable accuracy with reduced complexity compared to FFT-based methods. Additionally, when estimating the phase polynomial coefficients, the proposed filtering algorithm, combined with phase unwrapping and least-squares estimation, can achieve a mean square error (MSE) that is lower than or equal to that of the superior QML method, while significantly reducing computational complexity.

کلیدواژه ها:

Polynomial phase signal (PPS) ، polynomial coefficient estimation ، filtering algorithm ، frequency estimation