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Adaptive Sampling Strategy for near-field to far-field Transformation of Parabolic Antenna : Antenna measurement and near-field to far-field Transformation

파라볼릭 안테나의 근-원거리장 변환을 위한 적응형 샘플링 기법

  • 김원효 (서강대학교 일반대학원)

초록(요약문)

I propose an efficient adaptive sampling strategy for near-field to far-field （NF– FF） transformation of large parabolic antennas. Conventional near-field measurement- based far-field prediction methods require a wide measurement region and high spatial resolution, resulting in a large number of measurement samples and long measurement times. In particular, for large-scale antennas, the probe travel distance significantly affects the overall measurement time, making it difficult to achieve substantial improvements in measurement efficiency by simply reducing the number of samples. To address this issue, the near-field distribution is first approximated using a continuous function that combines a Gaussian mixture model and a cosine function. Based on this approximation, an adaptive sampling strategy utilizing Fisher information is proposed to select sampling locations that are important for near-field reconstruction. In addition, to overcome the limitation of Fisher information-based sampling regarding probe travel distance, an actor–critic-based reinforcement learning algorithm is introduced. The proposed reinforcement learning- based sampling strategy employs a reward function that simultaneously considers the information content of each sample and the movement distance from the previously selected sample location. Through this design, both information efficiency and movement efficiency are jointly optimized. Consequently, the proposed framework learns a sampling trajectory that maximizes long-term cumulative rewards throughout the entire measurement process. To validate the performance of the proposed method, simulations of a parabolic antenna using FEKO and measured data from horn and offset-parabolic antennas were employed. The Fisher information-based sampling strategy successfully reconstructed the near-field distribution using only a limited number of samples and accurately predicted the major far-field characteristics with fewer samples compared to conventional uniform and random sampling methods. Furthermore, the actor–critic-based sampling strategy more effectively reduced the cumulative probe travel distance compared to the Fisher information-based method, demonstrating superior performance in reducing the overall measurement time. In particular, the proposed method was shown to accurately predict key far-field characteristics, including main-lobe gain, 3 dB beamwidth, and sidelobe levels, even with highly limited near-field measurement data. Unlike conventional fixed sampling methods, the proposed adaptive sampling strategy dynamically determines the next sampling location based on the information acquired during the measurement process while simultaneously considering information acquisition efficiency and measurement movement cost. Therefore, this study presents a practical measurement framework capable of significantly reducing both the measurement time and operational cost of near-field measurements for large-scale antennas. The proposed method is expected to be applicable to high-frequency large-antenna measurement systems and real-time antenna performance verification in future applications.

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목차

I. Introduction 1
II. Background 7
2.1 Near-field to far-field transformation 7
2.2 Near-field construction using model-based parameter estimation 9
III. Fisher information based adaptive sampling strategy for near-field far-field transformation of parabolic antenna 11
3.1 Optimal sampling strategy based on Fisher information 11
3.2 Evaluation of proposed method through simulation of parabolic antenna 14
3.3 Evaluation of proposed method through measurements of horn antenna and offset parabolic antenna 24
3.3.1 Horn antenna measurement 24
3.3.2 Offset parabolic antenna measurement 29
3.3.3 Comparison between the measurement distance and the proposed method distance 34
IV. Actor-Critic-based adaptive sampling strategy for near-field to far-field transformation of parabolic antenna 36
4.1 Limitation of fisher information-based sampling strategy 36
4.2 Actor-Critic-based sampling strategy 38
4.3 Training of Actor-Critic 41
4.3.1 Data processing 41
4.3.2 Training 42
4.3.3 Verification through simulation 43
4.4 Verification using measurement data 48
4.4.1 Results for proposed method 48
4.4.2 Comparison between the actor-critic and the various sampling algorithms 51
V. Conclusion 53
Bibliography 55

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