# Detecting and Mitigating Attacks on GPS Devices
## 2 Existing Surveys
The authors of [15] focused on summarizing various path planning and obstacle avoidance algorithms from the literature, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Survey** \\ **Paper** \\ \end{tabular} } & \multicolumn{4}{c}{**Type of Survey**} & \multicolumn{4}{c}{**Alternate Positioning and Navigation Methods Discussed in the Survey**} \\ \cline{2-10}  & \begin{tabular}{c} **Technical** \\ **Approaches** \\ \end{tabular} & \begin{tabular}{c} **Performance** \\ **Analysis** \\ \end{tabular} & \begin{tabular}{c} **Research** \\ **Directions** \\ \end{tabular} & \begin{tabular}{c} **Visual** \\ **Based** \\ \end{tabular} & \begin{tabular}{c} **Visual** \\ **SLAM** \\ \end{tabular} & \begin{tabular}{c} **Lidar-** \\ **SLAM** \\ \end{tabular} & \begin{tabular}{c} **Algorithm** \\ **Evaluation** \\ \end{tabular} & \
\begin{tabular}{c} **AI/ML** \\ **Applications** \\ \end{tabular} &
\begin{tabular}{c} **IMU** \\ **Algorithm** \\ \end{tabular} \\ \hline
[11] & X & & X & X & X & X & & & X \\ \hline
[12] & X & X & & X & X & X & & X \\ \hline
[13] & X & X & & X & X & & & X \\ \hline
[14] & X & & & & X & X & & & \\ \hline
[15] & X & X & & & & & & X & \\ \hline
[16] & X & & & & X & X & & & \\ \hline
[17] & X & & & & X & & & & \\ \hline
[18] & X & X & X & & X & & & & \\ \hline
[19] & X & X & & & X & & & & \\ \hline
[20] & X & X & X & X & X & & X & & \\ \hline
[21] & X & & & & & & X & & \\ \hline
[22] & X & & & & & X & & X & \\ \hline
[23] & X & & & & & X & & & X \\ \hline
[24] & X & & & & & & X & & \\ \hline
[25] & X & X & & & & X & & & X & \\ \hline
[26] & X & & & & & & & X & \\ \hline This paper & X & X & X & X & X & X & X & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of alternate (non-GNSS) positioning and navigation survey papers. comparing the performance of various algorithms in terms of computational efficiency and the optimality of solutions for several scenarios and obstacle layouts. The analysis presented by the authors of [15] demonstrated that certain algorithms performed better in certain scenarios; however, there were cases when they had higher computational time or less optimal solutions than other techniques. The proper choice of algorithm should be based on operational requirements to best balance computational time and solution optimality. The authors of [16] provided a review of vSLAM and generalized vision-based navigation, focusing on visual methods for (1) map-based navigation systems, (2) obstacle detection and avoidance approaches, and (3) path planning-based approaches, highlighting the strengths and weaknesses of the various approaches. Future research challenges identified include (1) the need for improved scalability, (2) improved computational efficiency, (3) improved algorithm reliability, and (4) improved algorithmic robustness. Several vSLAM reviews discussed methods within a taxonomy based on the type of onboard visual sensor. The authors of [17] focused on approaches using monocular cameras. The survey discussed traditional visual SLAM methods, such as LSD-SLAM, ORB-SLAM, MSCKF, and DL-based methods. The authors stressed the difficulties of achieving real-time capability in vSLAM approaches and pointed out several key research directions in visual SLAM, including (1) the need for combined approaches that utilize IMU-based techniques, (2) the need for more work in incorporating DL-based techniques, and (3) the necessary mitigation of feature dependence, which the authors argue is the greatest limitation of vSLAM. The authors of [18] created one of the most comprehensive vSLAM reviews found in the literature, summarizing vSLAM methods across different types and numbers of visual sensors, including monocular vSLAM, stereo vSLAM, and RGB-D vSLAM. The authors also discuss different vSLAM methods, including event-based vSLAM, multi-modal vSLAM, and visual-inertial SLAM.