# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS jamming Detection
#### 5.1.1 Signal Statistics-Based Methods for Jamming Detection  
Examples of these methods include using changes in signal strength, power spectral density, or other statistical properties to identify the presence of a jammer. The authors of [67] proposed a method to detect and classify the type of jamming signals based exclusively on the statistical properties of the power spectral density (PSD) for the overall received signal. The authors of [67] demonstrated that the PSD of the composite GPSplus jammer signal will take on different shapes and statistical properties. Specifically, the proposed approach attempted to characterize the range of maximum PSD amplitude values for the received signal and then used PSD amplitude to characterize jammer presence and type. The advantage of this approach is its simplicity and ease of implementation in real-world GPS receivers; however, certain jammer signal types, specifically swept noise jamming, have PSD characteristics like the GPS signal itself, which would appear to make it difficult to detect this jammer type. The method proposed in [67] also does not seem to account for propagation loss between the jammer and target GPS receiver. It is unclear how one would necessarily know the appropriate range of maximum amplitude values for the received jammer signal PSD without this knowledge. A similar approach was proposed in [68], where the statistical properties of the received signal spectrum were used to detect the presence of a jammer. The fundamental assumption in this approach was that the jammer caused any variations in the received signal's statistics. Using this assumption, substantial changes in PSD mean or variance would be attributed to the presence of a jammer. It is unclear how this approach will perform in complex multipath fading and signal shadowing environments, where significant receive signal fluctuations will occur with or without the presence of jamming. Additionally, these approaches may perform well for high-power jammers, but it is unclear how they will perform when the received jammer power is roughly equivalent to the GPS signal. The authors of [69] proposed a somewhat similar approach to detect the presence of a jamming signal based on the overall RSS in the L1 GPS band. BER was considered in the jammer detection approach. The underlying assumption was that a rapid change in RSS or BER was likely due to the presence of a jammer signal. The proposed approach monitored and created a time-series history of RSS and BER for the L1 band. Jammer detection was declared when a rapid increase in RSS and BER was detected. The results presented in [69] suggested very good detection performance for this approach; however, this approach may be limited in scope and may not perform well in multipath and shadowing propagation environments where the actual GPS signal level may be rapidly fluctuating. The research in [69] focused on the use case of an oceanic surface ship attempting to detect GPS jamming. The receiver would have clear visibility into the GPS satellite constellation and would not rapidly fluctuate in that environment. Consequently, it may be a reasonable assumption that any rapid increase in BER is caused by interference; however, this assumption may not hold true in a complex environment that produces multipath fading and signal shadowing. Rapid rises in BER could just as easily be caused by signal shadowing or destructive signal fading. Rapid rises in RSS could be the result of constructive signal fading. A similar approach was proposed by the authors of [70], where a Moving Variance (MV) approach was proposed to detect the occurrence of jamming in the L1 band. This approach was predicated on the assumption that rapid variations in L1 carrier-to-noise (C/No) Density Power are likely attributable to a jammer. A rapid change in the sliding