研究目的
To investigate the contribution of random amplitude and phase errors at the elements of a phased array antenna to the degradation in the axial ratio of a circularly polarized phased array, and to develop and verify a formula for the variance of axial ratio as a function of these random errors.
研究成果
Random errors in phased array antennas degrade the axial ratio of circularly polarized arrays. A formula for the variance of axial ratio was developed and verified through Monte Carlo simulations, showing good agreement for small errors with equal standard deviations. This provides a predictive tool for assessing polarization performance in the presence of manufacturing and operational uncertainties.
研究不足
The approximation for axial ratio variance works well only when errors are small and both amplitude and phase errors have the same standard deviation. The model may not perform accurately when axial ratio exceeds 1.1 due to the small phase approximation, and it does not handle cases where amplitude and phase errors have different values effectively.
1：Experimental Design and Method Selection:
The study uses a theoretical model of a planar array of crossed dipoles to derive formulas for the impact of random errors on axial ratio. Monte Carlo simulations are employed to verify the derived expressions.
2：Sample Selection and Data Sources:
Simulations are based on Gaussian independent random errors with specified variances (e.g., amplitude error variance of
3：1V and phase error variance of 1 or 5 radians) for arrays with N=10 or N=100 elements. List of Experimental Equipment and Materials:
No specific equipment or materials are mentioned; the work is computational and theoretical.
4：Experimental Procedures and Operational Workflow:
The process involves deriving mathematical expressions for axial ratio variance, implementing Monte Carlo simulations with 1,000,000 random trials for each error scenario, and comparing simulation results with theoretical predictions.
5：Data Analysis Methods:
Statistical analysis includes calculating standard deviations and probability density functions (e.g., Rayleigh distribution) for axial ratio, and comparing estimated values with simulation outcomes.
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