Journal of Sensor Technology, 2013, 3, 115-125 Published Online December 2013 (http://www.scirp.org/journal/jst) http://dx.doi.org/10.4236/jst.2013.34018 Open Access JST Adaptive “Cubature and Sigma Points” Kalman Filtering Applied to MEMS IMU/GNSS Data Fusion during Measurement Outlier Hamza Benzerrouk 1 , Hassen Salhi 1 , Alexander Nebylov 2 1 SET Laboratory (Systèmes Electriques et Télecommande) of Electronic Department of Saad Dahlab University of Blida, Soumaa, Algeria 2 International Institute for Advanced Aerospace Technologies of Saint-Petersburg State University of Aerospace Instrumentation, 67 Bolshaya Morskaya, Saint Petersburg, Russia Email: hz_ben@hotmail.fr, hassensalhi@gmail.com, nebylov@aanet.ru Received October 12, 2013; revised November 25, 2013; accepted December 2, 2013 Copyright © 2013 Hamza Benzerrouk et al. This is an open access article distributed under the Creative Commons Attribution Li- cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT In this paper, adaptive sensor fusion INS/GNSS is proposed to solve specific problem of non linear time variant state space estimation with measurement outliers, different algorithms are used to solve this specific problem generally oc- curs in intentional and non-intentional interferences caused by other radio navigation sources, or by the GNSS receiver’s deterioration. Non linear approximation techniques such as Extended Kalman filter EKF, Sigma Point Kalman Filters such as UKF and CDKF are computed to estimate the navigation states for UAV flight control. Several comparisons are conduced and analyzed in order to compare the accuracy and the convergence of different approaches usually applied in navigation data fusion purposes. The last non linear filter algorithm developed is the Cubature Kalman Filter CKF which provides more accurate estimation with more stability in Tracking data fusion application. In this work, CKF is compared with SPKF and EKF in ideal conditions and during GNSS outliers supposed to occur during specific interval of time, innovation based adaptive approach is selected and used to modify the covariance calculation of the non linear filters performed in this paper. Interesting results are observed, discussed with real perspectives in navigation data fu- sion for real time applications. Three parallel modified algorithms are simulated and compared to non-adaptive forms according to Root Mean Square Error (RMSE) criteria. Keywords: IMU; MEMS; GPS; GNSS; Kalman Filtering; Cubature Rule; Sigma Points; Unscented Kalman Filter 1. Introduction Data fusion for non linear system is one of the most impor- tant and challenging problems in Multisensor signal proc- essing and integrated navigation systems today. In our work, sensors used are inertial as the main system with external aid provided by GPS and GLONASS receivers known recently as Global Navigation Satellite System “GNSS” solutions. Data fusion based on IMU/GNSS has been widely explored and experimented in the special- ized literature [1,2]. For inertial sensors, accelerometers and gyroscopes, the technology of manufacturing these sensors has a great importance and high impact on the accuracy of inertial navigation systems. In this paper, it is assumed that accelerometers and gyroscopes are in the category “Low cost” which gives more important interest in real time applications where most sensors are MEMS Micro Electrical Mechanical Systems based technology. The most inconvenient of these inertial sensors are the biases and drifts growing during time, which needs to be bounded by another technology of sensors such as used in our work, called GNSS receivers. Satellite-based sys- tems such as GNSS gives today’s satellite trajectory and high-precision navigation. Inertial sensors combined with GNSS receiver are a good alternative and reliable inte- grated system for navigation purposes. However, “GNSS”; Galileo E5a/E5b signals and the GPS L5 signal lie within the Aeronautical Radionavigation Services (ARNS) band. They suffer interference from the services in this frequency band, in particular, high power pulsed signals from Distance Measuring Equipment (DME) and Tactical Air Navigation (TACAN) systems embeeded on most aircrafts. The pulsed interference degrades received