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An Efficient Frequency Estimator for a Complex Exponential Signal Based on Interpolation of Selectable DTFT Samples
Miaomiao Wei1,2, Aihua Zhang1, Lin Qi2
1Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
Sensors (Basel, Switzerland)
|February 15, 2022
Summary
This study introduces an iterative frequency estimator for complex exponential carrier signals in noise. The new method achieves a root mean square error closer to the theoretical Cramér-Rao lower bound.
Area of Science:
- Signal Processing
- Digital Communications
- Statistical Inference
Background:
- Accurate frequency estimation of complex exponential carrier signals is crucial in various signal processing applications.
- Existing methods face challenges in achieving high accuracy, especially in noisy environments.
Purpose of the Study:
- To develop a novel iterative frequency estimation algorithm for complex exponential carrier signals.
- To analyze the performance and error characteristics of the proposed estimator.
- To provide guidance on parameter selection for optimal estimation accuracy.
Main Methods:
- Iterative computation of Discrete-Time Fourier Transform (DTFT) samples for fine frequency estimation.
- Mean Square Error (MSE) analysis to evaluate estimator performance.
- Parameter influence analysis to optimize estimation accuracy.
Main Results:
- The proposed iterative estimator achieves fine frequency estimates.
- MSE analysis demonstrates the estimator's performance characteristics.
- Guidance on parameter selection is provided to minimize estimation error.
- Simulation results show the estimator's Root Mean Square Error (RMSE) approaches the Cramér-Rao Lower Bound (CRLB).
Conclusions:
- The novel iterative frequency estimator offers improved accuracy for complex exponential carrier signals in noise.
- The method provides a valuable tool for signal processing applications requiring precise frequency estimation.
- The estimator's performance is competitive, achieving results close to the theoretical CRLB.
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