# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.2 Landmark-Based Approaches
Numerous studies in the literature demonstrate that good position accuracy can be obtained using 4G LTE cellular SOPs [135; 136; 137; 138; 139; 140; 141]. There is also a growing amount of literature demonstrating that good performance can be obtained using 5G cellular SOPs [142; 143; 144]. Previous work has demonstrated accurate positioning using CDMA-based cellular SOPs [145]. Cellular SOPs could be cooperative or uncooperative. A cooperative SOP paradigm means that the system is a participating member of the cellular network. This approach allows the User Equipment (UE) to receive positioning information from the cellular network via its existing positioning method, such as the LTE Positioning Protocol (LPP) in 4G cellular networks. Cellular UE positioning via these built-in positioning methods and the high accuracy of these methods are well understood; however, research has suggested that the performance of these methods may be sensitive to jamming and interference [143]. This approach may not work in regions without significant cellular infrastructure, such as unpopulated desert regions. There are also potential negative impacts on the overall cellular network associated with UAS-based UEs. This issue is due to the increased visibility of the cellular network by the UAS-based UE, potentially increased uplink interference, and a higher number of system handoffs [146]. The second approach is uncooperative in nature, where the system is equipped with an RF receiver capable of passively observing cellular signals and using them as landmarks but is not a valid user of the cellular network. A fundamental concern with RF-landmark-based approaches is that these signals are all unauthenticated, and while publicly available information is valuable in developing an onboard knowledge base, it is also valuable to someone launching a malicious attack. It is feasible that someone could mount an attack where they not only spoof GPS but also any of the signals mentioned above since SDR technology is easy to obtain due to its convenience and low cost. This aspect of RF-landmark research has not been addressed in the literature to date. Visual landmark-based approaches are predicated on detecting landmarks in imagery captured via a camera sensor. These landmarks can be either manmade or natural and, combined with some type of onboard knowledge base, could be used to determine position with high demonstrated accuracy [127; 147]. An example of landmark preplacement for visual recognition in support of navigation can be found in [148], where landmarks are strategically established in a partially known environment to support robot navigation. Another example can be found in [149], where the authors used a monocular camera onboard a robotic ground vehicle to detect pre-placed landmarks that served as waypoint markers. The generalized applicability of these approaches is limited due to their reliance on well-known existing landmarks or pre-placed objects along the path of movement; however, these approaches may be useful when operating in known areas or fixed flight paths. These types of approaches could also be helpful in hybrid approaches. Combining visual landmark-based approaches with IMU-based approaches can achieve high position estimation accuracy [115], where visual recognition provides intermittent high-accuracy ground-truth position estimates that can be used for IMU accumulation error reset. The authors of [115; 127] demonstrated that this type of approach can work well within sufficiently dense regions with visually identifiable landmarks. For example, consider using roads as landmarks, where a UAS equipped with a monocular camera detects roads within images taken from the camera and recognizes the ground road pattern to determine its own position. This approach has been proposed by multiple papers in the literature [150; 151], with good position accuracy performance reported; however, landmark-based approaches do not perform well in feature-sparse regions, such as deserts and oceans.