Performance Optimization in Frequency Estimation of Noisy Signals: Ds-IpDTFT Estimator

Area of Science:

• Signal Processing
• Estimation Theory

Background:

• Accurate frequency estimation is crucial in signal processing, especially for noisy exponential signals.
• Existing methods often face challenges with computational complexity and accuracy in noisy environments.

Purpose of the Study:

• To introduce and evaluate a novel dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator.
• To assess the estimator's performance in terms of accuracy, computational complexity, and robustness.

Main Methods:

• The Ds-IpDTFT estimator combines a dichotomous search with iterative parabolic interpolation.
• The methodology investigates the relationship between optimal parameter p and unknown parameter δ.
• Performance is validated through extensive simulations and real-world experiments.

Main Results:

• The Ds-IpDTFT estimator demonstrates high estimation accuracy and reduced computational complexity.
• It exhibits superior performance compared to established estimators.
• The estimator shows robustness in noisy conditions and stability across varying frequencies.

Conclusions:

• The Ds-IpDTFT estimator is an efficient and accurate method for fine frequency estimation.
• It offers significant advantages over existing techniques, particularly in noisy signal environments.
• This novel approach has promising potential for practical signal processing applications.