Massive MIMO (multiple-input, multiple-output) base stations are widely used for wireless networks to deploy multiple antennas, increasing their quality, throughput, and radio link capacity. Unmanned aerial vehicles (UAV) are prevalent due to their low cost and ease of use. Unmanned aerial vehicles (UAV) are prevalent due to their low cost and ease of use, allowing for multiple use cases that provide telemetry information to civilian, commercial, and military applications. In particular, we implement a suite of Angle of Arrival (AoA) estimation algorithms exploring their performance for UAV communication networks. From the evaluation of the five AoA estimations, Beamscan offers a spatial, spectral response that enables us to analyze both secondary propagation paths and the most likely AoA, providing us with a complete picture of the environment. We discovered with convergence time that when under-sampled, the AoA estimator detects the multi-path with a higher normalized power, impacting the AoA estimate result. We estimated azimuth AoA via horizontal subarrays and its effects on the multi-path AoA estimates for hovering drones. We discovered the effects of Rice football stadium seats as we decreased the number of antennas. We find that when evaluating hovering drones' azimuth and elevation AoA estimation, elevation estimation yields a median error of 13.8\degree higher error than azimuth for the 5x5 antenna scenario. Evaluated was the performance of the 2-D Beamscan spatial spectrum estimator. It provides higher accuracy between the two different channels of azimuth and elevation. This work will inform system designers on specifications from AoA estimations when designing a Massive MIMO to drone network.