# Detecting and Mitigating Attacks on GPS Devices
## 7 Research Gaps, Challenges, and Future Research Directions
Research is needed to reduce the computational complexity of visual methods of positioning and navigation. Furthermore, methods to improve the performance of visual methods in low-light and changing environmental conditions should be explored. Star tracker approaches should mature through future research. This research area is relatively dormant, but ongoing research is promising, and we believe it could represent an opportunity to provide a reliable means of navigation for many unmanned systems. Currently, no existing approaches provide universally good performance with reasonably low complexity. Some promising techniques are emerging; however, those most promising techniques would be susceptible to many of the same threats faced by GPS. We have not yet converged on a mature technical solution to this problem due to the wide range of existing methods and approaches, all of which have strengths. Every existing method and approach work well in some situations but poorly in others; therefore, it is possible that there is no single best solution to this problem and that ensemble solutions will be required. From the literature, it appears that researchers, in general, have come to this realization. Most of the recent research papers focused on multi-sensor fusion approaches, where multiple sensor inputs were used jointly to develop position and navigation solutions. It is expected that this trend will continue. One limitation of the current multi-fusion approaches is that they are almost universally focused on two particular sensor data types and are not expandable or modular to incorporate new sensor types. A key future research area would be the development of a generalized multi-sensor fusion framework that could accommodate any sensor type possibly encountered in a plug-and-play fashion. This envisioned framework could operate with any subset of the total possible sensors without configuration or tailoring, which is vital because every UAS platform will likely have a slightly different hardware configuration with different sensors and processing capabilities. The standard approach to multi-sensor fusion is to input data from multiple orthogonal sensors into a Kalman filter to fuse the data and generate a state estimate of the system. The Kalman filter excels at fusing inputs that contain uncertainty due to noise; however, it struggles when a sensor returns anomalous data. Various sensors and approaches have strengths and weaknesses and may produce data that are not relevant in certain environments and conditions, leading to anomalous data that could negatively harm traditional data fusion approaches. Future multi-sensor fusion methods should consider the relevance of sensor data. A key trend observed in the literature is that recent research across all aspects of detection and mitigation is adopting ML-based approaches, which is expected since ML-based approaches can yield very good results; however, ML-based approaches can be computationally expensive and may not be well-suited for smaller and low-cost UAS platforms. Research into applying sparse dataset training methods, such as zero-shot detection for visual methods, may prove useful. Finally, based on the strengths and weaknesses of methods found in existing literature, the community has likely not yet converged on an optimal solution, and we must continue to further explore new detection and mitigation strategies against the threats faced by GPS. Conceptualization, J.B.; methodology, J.B. and N.K. ; investigation, J.B.; writing--original draft preparation, J.B.; writing--reviewing and editing, J.B., T.G. and N.K. ; supervision, N.K. ; project administration, N.K. ; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the support of the National Science Foundation (NSF), Award Number 2006674. The authors declare no conflict of interest.