# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.1 IMU-Based Approaches
The authors of [118] proposed an integration of IMU outputs with magnetometer sensor outputs to improve accuracy in attitude initialization. Numerous papers have proposed fusing IMU-based techniques with various sensors that may be onboard the UAS, including vision techniques utilizing an onboard monocular camera [119], altitude estimation using a range sensor [120], SLAM techniques using LiDAR [121], and LEO satellite tracking [122]. Several research papers also propose a joint IMU-GPS positioning solution to improve performance where GPS reception is intermittent or inaccurate, such as in urban environments [43; 123]. These papers report low-achieving position estimation errors, generally within 10 m. The primary disadvantage of these proposed methods is the need to include additional sensors onboard the platform. Few studies have proposed ML-based approaches for nonlinear processing to minimize inertial drift. For example, the authors of [124] proposed a method where IMU sensor output error would be estimated and corrected by employing a non-linear auto-regressive neural network with exogenous inputs cascaded with a multi-layer perceptron (MLP)-based neural network. The proposed approach outperformed recurrent neural network (RNN)-based and legacy EKF approaches. The authors of [124] reported position estimation accuracy improvements over tactical-grade IMUs of 30%, 44%, and 80% for GPS outages of 10, 25, and 50 s. These results are encouraging; however, error drift still limits the useful timeframe for navigation based on IMUs alone. Redundant IMU and multi-sensor fusion methods offer good performance with a low cost of computational complexity. The major disadvantage of these approaches is the need for additional sensor hardware onboard the UAS, which may be problematic for small and low-cost systems; however, there are examples of multi-sensor fusion approaches working on small micro-UASs. The authors of [125] demonstrated a multi-sensor fusion navigation system onboard a micro-UAS that utilized 3D LiDAR, a stereo camera, an altimeter, and IMU sensors, with data fused using a synchronization and time delay compensation algorithmic strategy. This research demonstrated that these multi-sensor approaches are achievable on small UAS platforms. A technical challenge not addressed in the literature is the initial determination of the ground-truth position. IMU-based methods provide relative position information and must have an accurate ground truth from which to start. Onboard sensors may prove less useful in providing this ground truth. This problem may require manual intervention if the unmanned system is initiated from a well-known location. There have been proposed methods that rely solely on magnetometer sensors for navigation [126]. The MagNav concept uses Earth's magnetic field to uniquely identify any point globally. Good performance has been observed using MagNav approaches, reaching the destination of a 1500-mile flight to within 1 km of accuracy using nothing but a magnetometer and machine learning algorithms trained on magnetic field map data. A significant challenge with this approach is that Earth's magnetic field is constantly changing, requiring periodic magnetic field surveying and model retraining.