Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 51 1 2019 06 01 Unmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing 93 100 3099 10.22060/eej.2018.14934.5248 EN Sayeh Mirzaei School of Engineering Science, College of Engineering, University of Tehran Alireza Gholipour Department of electrical engineering, Shahid Beheshti University 0000-0001-6992-8112 Journal Article 2018 09 05 Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A practically optimal scanning strategy is presented. The suggested techniques are beneficial particularly for the case of large directive antennas. The UAV follows a predefined trajectory in the scanning windows around the AUT and reads the field strength. Then, using compressed sensing (CS) method, the antenna pattern is reconstructed. It is shown that applying Bayesian CS algorithm to the samples of field intensity gathered by UAV can efficiently reconstruct the pattern. Discrete cosine Transform (DCT) is utilized for sparsifying the antenna patterns. Performance is evaluated by obtaining the reconstructed patterns for different antenna types. The effects of the antenna type and area of scanning are analyzed. It is shown that satisfying performance can be achieved with measuring about 50 percent of the total pattern samples. The reconstruction error of different CS implementations is computed and superiority of Bayesian CS is illustrated. https://eej.aut.ac.ir/article_3099_b23f52202479e957b9bada847c1175d7.pdf