A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

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 receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted 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 integration 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 estimating 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.

[1]  Yih-Lon Lin,et al.  A particle swarm optimization approach to nonlinear rational filter modeling , 2008, Expert Syst. Appl..

[2]  Chimpalthradi R. Ashokkumar,et al.  Sensor fusions for constant thrust aircraft navigation in pitch plane , 2018 .

[3]  M. Moazedi,et al.  Analysis of Single Frequency GPS Receiver Under Delay and Combining Spoofing Algorithm , 2015, Wirel. Pers. Commun..

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  Kin Huat Low,et al.  Adaptive Visual Servoing of Unmanned Aerial Vehicles in GPS-Denied Environments , 2017, IEEE/ASME Transactions on Mechatronics.

[6]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[7]  Seong Yun Cho,et al.  Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System , 2013 .

[8]  Bin Jiang,et al.  Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments , 2015 .

[9]  Jhon A. Isaza-Hurtado,et al.  State Estimation Using Non-uniform and Delayed Information: A Review , 2018, Int. J. Autom. Comput..

[10]  Benjamin J. Southwell,et al.  Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor , 2013 .

[11]  Rong Wang,et al.  A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations , 2016 .

[12]  Gérard Lachapelle,et al.  SPOOFING COUNTERMEASURE FOR GNSS RECEIVERS - A REVIEW OF CURRENT AND FUTURE RESEARCH TRENDS 4 th International Colloquium on Scientific and Fundamental Aspects of the Galileo Programme European Space Agency, Prague, 4-6 December 2013 , 2013 .

[13]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[14]  Otmar Loffeld,et al.  INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter , 2010 .

[15]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[16]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[17]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[18]  Sanguk Lee,et al.  Analysis of effect of spoofing signal in GPS receiver , 2012, 2012 12th International Conference on Control, Automation and Systems.

[19]  Juan Vasquez,et al.  Detection of Spoofing, Jamming or Failure of GPS , 1993 .

[20]  Mathieu Joerger,et al.  GPS spoofing detection using RAIM with INS coupling , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[21]  Abd Rahman Ramli,et al.  Intelligently tuned wavelet parameters for GPS/INS error estimation , 2011, Int. J. Autom. Comput..

[22]  R. Wilcox.  Introduction to Robust Estimation and Hypothesis Testing , 1997 .

[23]  Sergio Montenegro,et al.  An Autonomous UAV with an Optical Flow Sensor for Positioning and Navigation , 2013 .

[24]  R. Katulski,et al.  Detection and Mitigation of GPS Spoofing Based on Antenna Array Processing , 2015 .

[25]  Maamar Bettayeb,et al.  Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter , 2017, International Journal of Automation and Computing.

[26]  Srdjan Capkun,et al.  SPREE: a spoofing resistant GPS receiver , 2016, MobiCom.

[27]  Aboelmagd Noureldin,et al.  Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration , 2012 .

[28]  Aleksandar Subic,et al.  Matrix weighted multisensor data fusion for INS/GNSS/CNS integration , 2016 .

[29]  Jongwoo An,et al.  Robust Navigational System for a Transporter Using GPS/INS Fusion , 2018, IEEE Transactions on Industrial Electronics.

[30]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..