# Detecting and Mitigating Attacks on GPS Devices
## 2 Existing Surveys
The other survey papers [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] described non-GPS methods of positioning and navigation. There are many surveys on navigation and positioning in GPS-denied and GPS-disrupted environments; however, these surveys typically focus on the narrow aspects of positioning and navigation based on specific sensor types or methods. Many surveys focused on aspects of Simultaneous Location and Mapping (SLAM), discussing how autonomous systems learn and map an area while determining position within that area. SLAM most commonly uses some combination of optical sensors, such as cameras and Light Detection and Ranging (LiDAR) sensors. Many papers in the literature focused on the visual methods of positioning, where a system uses a camera to detect known landmarks within its surrounding environment. Other surveys focused on Inertial Measurement Unit (IMU)-based positioning approaches, where sensors such as accelerometers and gyroscopes are used to track a system's motion to determine relative position. Other papers in the literature focused on RF-based methods of positioning and satellite-based (non-GNSS) approaches. Several surveys related to positioning in GPS-denied environments focused more on summarizing the approaches found in the literature and did not provide a performance analysis of the approaches, discuss research gaps, or provide recommendations for future research directions. Table 2 provides a summary of those surveys that focused on positioning and navigation in GPS-denied environments and categorizes these papers in terms of the type of survey, creating a taxonomy with three high-level categories: (1) summarizing approaches in the literature on positioning and navigation in GPS-denied environments; (2) analyzing the effectiveness of alternative positioning and navigation methods; and Visual SLAM, or vSLAM, has received the most attention in existing reviews. The authors of [11; 12] provided comprehensive reviews of GNSS-independent navigation methods for autonomous vehicles, and further reviews of vSLAM methods are prevalent in the literature. Many vSLAM-focused reviews significantly overlap the techniques they discussed and simply present them within different taxonomies; however, several surveys focused on different aspects of vSLAM. The authors of [11] focused on assessing the technology maturity and reliability of various SLAM methods, including visual and laser-based approaches, with IMU sensor assistance for two-dimensional and three-dimensional mapping applications. The authors of [12] reviewed existing methods within a taxonomy of map-based navigation, where a region's map is known a priori and the system must determine its position within the known map, and mapless navigation, where little-to-know information about the region is known a priori. The authors of [13] presented one of the few reviews that discuss absolute visual localization (AVL) techniques, with a focus on UAS applications. AVL techniques provide absolute location in the form of latitude and longitude instead of relative location within some arbitrary reference system. Most existing surveys focused on relative visual localization (RVL) methods, in which the system's location is only determined within a non-absolute reference system, typically relative to other objects or waypoints. The core issue surrounding RVL is error accumulation, also known as drift over time. This issue can be somewhat alleviated through IMU-based enhancements; however, these approaches do not correct the underlying problem or applications that require long-term precision positioning. The authors of [14] discussed approaches for specific functions within the overall vSLAM problem space, including obstacle detection, obstacle avoidance, and path planning, within the context of mapless navigation approaches such as performing navigation, without a known map, map-based navigation approaches, and map-building approaches.