# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 1 Introduction  
Antenna pose has always played an important role in cellular network planning and optimization, from the era of the 2G network [1] to the present day (e.g., [2, 3]). It directly affects signal coverage, soft handover and interference between cells [4] and indirectly affects other network performance indicators, such as quality of service [5], and network configuration parameters, such as transmission power [6]. Thus, determining the pose of an antenna during installation and monitoring its subsequent changes in pose are important tasks. The pose of an antenna is typically parameterized in terms of its downtilt (or elevation) and azimuth angles (e.g., in [4]), which are formally defined with respect to the direction of themain lobe [7]. For the time being, there are two popular approaches to measure the antenna pose in the industry. The first method is to measure the downtilt and azimuth angles manually by a person using an inclinometer and a compass; the second one is to employ specialized sensors, such as the Antenna WASP [8] from 3Z TelecomTM, or portable measurement devices equipped with internal sensors, such as the antenna alignment tool (AAT) [9] from SunlightTM, to facilitate the measurement process. However, both methods have their limitations. For the manual measurement, because numerous antennas are mounted on towers that are high off the ground and electrically powered, reaching these antennas takes much effort and poses a high risk for workers. Moreover, it is difficult to guarantee the accuracy and precision of such manual measurements because of individual differences among workers. As for the second solution with sensors, on the one hand, since a single sensor unit like the WASP costs a few tens of U.S. dollars, the gross overhead becomes enormous when the total number of antennas is considered for a large mobile network; on the other hand, portable measurement devices like AAT are usually expensive, and workers still must physically access the antennas to use them. The recent proliferation of mobile phones with various types of built-in sensors, especially cameras and inertial measurement units (IMUs), has given rise to a wide range of interesting new applications and algorithms [10] that rely on the fusion of visual and inertial information for use in many fields: for example, object recognition [11], 3D reconstruction [12; 13], tracking [13; 14] and pose estimation [15]. These studies have inspired us to propose a novel non-contact solution to the measurement problem of the antenna pose in the Earth frame using the camera and IMU data from mobile phones. Two technical challenges arise when designing this non-contact approach. First, antennas are only sparsely textured and usually have simple shapes with smooth surfaces (see Figure 1a), providing a few of the distinctive features and stable matches (see Figure 1b,c) that are usually required for existing feature-based pose estimation methods. Second, the IMU sensors in mobile phones are usually ultra-low-cost (consumer-level) microelectromechanical system (MEMS) sensors with poor accuracy [16; 17; 18]; however, accuracy is of key importance for industry applications, such as network optimization [19]. To address these challenges, we design and develop our solution based on adequate consideration of the characteristics of antennas and the sensors in mobile phones. First, we introduce a 3D antenna model and describe the visual pose estimation problem for an antenna as a direct 2D-3D matching problem based on the outer contours of the antenna to avoid the influence from the antenna's lack of geometric and textural features. This approach requires prior knowledge of the antenna's 3D geometry, but this is not yet an excessive requirement because of the limited number of different antenna products that are currently in use.