Sparse Matrix Motivated Reconstruction of Far-Field Radiation Patterns
Final rept. Aug 2015
ARMY RESEARCH LAB WHITE SANDS MISSILE RANGE NM SURVIVABILITY/LETHALITY ANALYSIS DIRECTORATE
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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.
- Radiofrequency Wave Propagation