Sparse Matrix Motivated Reconstruction of Far-Field Radiation Patterns

The measurement of radiation patterns is time consuming and expensive. Therefore, a novel technique that reduces samples required to measure radiation patterns is proposed where random samples are taken to reconstruct 2-dimensional (2-D) or 3-dimensional (3-D) far-field radiation patterns. The proposed technique uses a compressive sensing algorithm based on sparse representations of radiation patterns using the inverse Discrete Fourier Transform (DFT) and the inverse Discrete Cosine Transform (DCT). The algorithm was evaluated by using 3 antennas modeled with the high-frequency structural simulator (HFSS): a half-wave dipole, a Vivaldi, and a pyramidal horn. The 2-D radiation pattern was reconstructed for each antenna using less than 44% of the total number of measurements with low-root mean square error (RMSE). In addition, the proposed reconstruction algorithm was evaluated using measured data obtained in an anechoic chamber. The 3-D radiation patterns of a pyramidal horn antenna was reconstructed by using only 13% of the total number of measurements. By using the proposed approach for radiation pattern reconstruction, the time required to take measurements in an anechoic chamber can be reduced up to 87%, therefore ensuring a good reconstruction with very low-RMSE in the case of a directive antenna such as the pyramidal horn. The original document contains color images.