Localization in GNSS-denied environments for Unmanned Aerial Vehicles (UAVs) has recently gained significant interest from the research community. Most of the research is focused primarily on visual localization. This paper, examines an algorithm which employs Angle of Departure (AoD) and UAVs payload sensor data for UAV localization. First the algorithm uses multiple AoDs from a single base station and a travel calculated by applying dead-reckoning on the UAVs Inertial Measurement Unit (IMU), to compute UAV location in two-dimensional (2D) coordinates. The 2D location estimate is then fed into a modified Extended Kalman Filter (EKF), which employs the estimate, IMU and barometer data to compute the three-dimensional (3D) coordinates for UAV. For the simulation, we applied Simulation-in-the-Loop (SITL) accompanied by Arducopter and MAVLink to simulate different trajectories and collect the required data for the algorithm. We validated our algorithm by comparing the EKF estimates with IMU dead-reckoned positions. Three simulations were performed, consisting of linear, zigzag and curved trajectories. We achieved a 90th percentile error of 2.5m and 4m for the x-coordinate and y-coordinate, respectively, on the zigzag and curved trajectories. Interestingly, the linear trajectory showed a larger localization error in its y-coordinate.