Polynomial Phase Estimation Based on Adaptive Short-Time Fourier Transform.

Area of Science:

• Signal Processing
• Statistical Signal Estimation

Background:

• Polynomial Phase Signals (PPSs) are crucial in radar, sonar, geophysics, and radio communications.
• Accurate estimation of PPS coefficients is vital for these applications.

Purpose of the Study:

• To propose a novel and efficient Polynomial Phase Signal parameter estimation method.
• To overcome limitations of existing methods, such as multi-dimensional searches and error propagation.

Main Methods:

• Introduced the Polynomial Phase Signal-Adaptive Short-Time Fourier Transform (PPS-ASTFT) estimator.
• Utilized S-transform (ST) for instantaneous frequency (IF) estimation, preserving phase information.
• Determined adaptive window width using instantaneous frequency gradient (IFG) calculated via robust principal component analysis (PCA).
• Incorporated a refinement strategy to enhance estimation accuracy.

Main Results:

• The PPS-ASTFT estimator successfully avoids one-dimensional and multi-dimensional searches and error propagation.
• The method demonstrates robustness to noise due to PCA-based IFG calculation.
• Numerical simulations confirm excellent statistical performance and efficiency compared to traditional methods.

Conclusions:

• The proposed PPS-ASTFT estimator provides an accurate, efficient, and robust solution for Polynomial Phase Signal parameter estimation.
• This method offers significant advantages for applications requiring precise signal analysis in challenging environments.