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INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

Public [article]  inGPS solutions > vol 24 n° 1 (January 2020) Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification floue [Termes IGN] couplage GNSS-INS [Termes IGN] données GNSS [Termes IGN] filtre de Kalman [Termes IGN] interruption du signal [Termes IGN] ondelette [Termes IGN] réseau neuronal artificiel [Termes IGN] réseau neuronal récurrent [Termes IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458 [article]

Design and implementation of a model predictive observer for AHRS / Jafar Keighobadi in GPS solutions, vol 22 n° 1 (January 2018)  

Public [article]  inGPS solutions > vol 22 n° 1 (January 2018) Titre : Design and implementation of a model predictive observer for AHRS Type de document : Article/Communication Auteurs : Jafar Keighobadi, Auteur ; Hamid Vosoughi, Auteur ; Javad Faraji, Auteur Année de publication : 2018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] angle d'Euler [Termes IGN] attitude and heading reference system AHRS [Termes IGN] erreur instrumentale [Termes IGN] erreur systématique [Termes IGN] estimateur [Termes IGN] filtre de Kalman [Termes IGN] GPS-INS [Termes IGN] microsystème électromécanique [Termes IGN] véhicule Résumé : (auteur) A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation‚ position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises‚ the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov’s direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore‚ we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF. Numéro de notice : A2018-017 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-017-0696-4 En ligne : https://doi.org/10.1007/s10291-017-0696-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89114 [article]