###### Abstract  
Applications of drones have increased significantly in the past decade for both indoor and outdoor operations. In order to assist autonomous drone navigation, there are numerous sensors installed onboard the vehicles. These include Global Navigation Satellite Systems (GNSS) chipsets, inertial sensors, barometer, lidar, radar and vision sensors. The two sensors used most often by drone autopilot controllers for absolute positioning are the GNSS chipsets and barometer. Although, for most outdoor operations, these sensors provide accurate and reliable position information, their accuracy, availability, and integrity deteriorate for indoor applications and in the presence of radio frequency interference (RFI), such as GNSS spoofing and jamming. Therefore, it is possible to derive network-based locations from Wi-Fi and cellular transmission. Although there have been many theoretical studies on network positioning, limited resources are available for the expected quantitative performance of these positioning methodologies. In this paper, the authors investigate both the horizontal and vertical accuracy of the Android network location engines under rural, suburban, and urban environments. The paper determines the horizontal location accuracy to be approximately 1637 m, 38 m, and 32 m in terms of 68% circular error probable (CEP) for rural, suburban, and urban environments, respectively, and the vertical accuracy to be 1.2 m and 4.6 m in terms of 68% CEF for suburban and urban environments, respectively. In addition, the availability and latency of the location engines are explored. Furthermore, the paper assesses the accuracy of the Android network location accuracy indicator for various drone operation environments. The assessed accuracies of the network locations provide a deeper insight into their potential for drone navigation. A Article Assessment of Android Network Positioning as an Alternative Source of Navigation for Drone Operations  
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Footnote 12: email: {dmgkyeong. with respect to the standard atmospheric pressure or a local reference station. Although this suite of sensors provides accurate PVT to the users in nominal outdoor environments, they need additional sensors to navigate indoor environments or when in the presence of radio frequency interference (RFI). This is because under these conditions, GNSS may be unavailable or not trustable. Consequently, numerous localization methodologies have been developed to complement these sensors and assist in drone navigation, one of which is network positioning. Unlike methodologies, such as simultaneous localization and mapping (SLAM) or ground-based localization systems, network positioning does not require a large onboard computational load, and a lot of the necessary infrastructure is already available in areas with Wi-Fi or cell service. Network positioning is a means of absolute positioning that is popular in smartphones, but has had limited exploitation in autonomous vehicle operations. In order to assess the potential of network positioning for drone applications, the authors investigated the accuracy and the characteristics of the network location provider (NLP) in Android devices. This was preliminarily presented by [PERSON], but a more extensive investigation is presented within this paper [PERSON] _et al._ (2018). The accuracy of the NLP was tested under rural, suburban, and urban environments with varying numbers of available Wi-Fi and cell access points. For the positioning assessment, the authors attempted to characterize the accuracy of, not only the horizontal positions, but also the altitude provided by the NLP. Afterwards, the accuracy of the NLP was quantified with respect to the number of Wi-Fi and cell access points available. These accuracies were compared with Google's NLP accuracy estimates to see how well the estimates bound the errors, and, if necessary, how much they need to be inflated by before being passed on to the drone's flight controller for navigation error bounding.