Gain prediction and compensation for subarray antenna with assembling errors based on improved XGBoost and transfer learning
Fang Guo, Zhenyu Liu, Weifei Hu, Jianrong Tan
Abstract
Large array antennas are often assembled with several subarrays, and assembling errors containing position and orientation are the key factors determining the final radiation pattern. As an important feature of radiation patterns, the antenna gain significantly influences the design of the antenna. However, little research has been conducted on the accurate prediction and detailed compensation of gain with assembling errors. In this work, the authors propose an accurate gain prediction model using an improved extreme gradient boosting (XGBoost) algorithm and the transfer learning method. Knowledge from both the simulation data and experience is converted to weights to help train the improved XGBoost model. Experimental data are then used to modify the model for complex factors, such as mutual coupling and element type. Compensation methods are proposed to provide directions to limit the degradation of the gain within a range by controlling the assembling errors. Experiments are conducted on a platform with a 3 × 3 subarray antenna. The results indicate that the proposed gain prediction model is more accurate than the model developed using artificial neural network, support vector regression, and existing XGBoost algorithms. The steps of gain compensation are also reduced with the proposed compensation methods.