A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions Mohammad Majidi 1 Alireza Erfanian 2 Hamid Khaloozadeh 3 1 Department of Electrical Engineering, Malek-Ashtar University of Technology, Tehran 15875-1774, Iran 2 Faculty of Electrical Engineering, Malek-Ashtar University of Technology, Tehran 15875-1774, Iran 3 Faculty of Electrical Engineering, K.N.Toosi University of Technology, Tehran 16315-1355, Iran Abstract: This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS re- ceivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying ad- apted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle′s experiences, application of PSOF algorithm in the INS/GPS integ- ration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estim- ating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test. Keywords: Inertial navigation system (INS)/global positioning system (GPS) integration, unmanned aerial vehicles (UAVs), position estimation, spoofing, particle based filters. 1 Introduction Nowadays, many factors threaten the global position- ing system (GPS) receivers installed in the unmanned aerial vehicles (UAVs). GPS spoofing is one of the most important threats which deviates the positioning process in GPS receivers. When the flying UAV is in GPS spoof- ing threat condition, there will be some mistakes in posi- tion calculation in the GPS receivers. Therefore, the nav- igation data (position data generated by UAV navigation system) which are sent through data-link to the central monitoring station is counterfeited and the UAV will be lost and crashed. So, the significance of estimation in the process of UAV true positioning in the conditions of spoofing attacks is well established. The basic navigation systems in UAVs are inertial navigation system (INS) and GPS which carry out the positioning process. The INS provides position, velocity and attitude of vehicles with good short term accuracy. The performance of INS is enhanced due to performance enhancement of microelectromechanical systems (MEMS), but it has unbounded long term errors which increase in time during its performance that are called drift error. Unlike the INS, GPS has good long term accuracy with bounded errors in few meters. By considering comple- mentary characteristics of these two systems, their integ- ration eliminates their individual drawbacks which leads to accurate and robust navigation solution. So, GPS elim- inates the drift error of INS and INS helps to provide continuous navigation solution [1, 2] . With respect to the noises of sensors and measuring devices, integration of systems or sensors is suggested in [2–4]. On the other hand, data fusion (DF) algorithms are used in several fields such as system integration [5] , air- craft navigation [6] , autonomous UAV positioning [7] , ro- bust navigational system [8] , wheelchair navigation [9] , de- noising INS and GPS data [10] . Therefore, to improve the accuracy, redundancy and reliability of navigation sys- tems in noisy environments, DF algorithms are used in INS/GPS integration system. INS/GPS integration in navigation systems is based on data fusion. In addition, variable state estimation to find dynamic error in the integration of INS/GPS sys- tems is done through linear and nonlinear DF algorithms. Research Article Manuscript received September 22, 2017; accepted May 29, 2018 Recommended by Associate Editor Min Cheol Lee © Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature 2018 International Journal of Automation and Computing DOI: 10.1007/s11633-018-1137-8